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Tethered quadcopters are used for extended flight operations where the powerto the system is provided via a tether connected to an external power source.In this work, we consider a system of multiple quadcopters powered by a singletether. We study the design factors that influence the power requirements, suchas the electrical resistance of the tether, input voltage, and quadcopters'positions. We present an analysis to predict the required power to be suppliedto a series of N tethered quadcopters, with respect to the thrust of eachquadcopter which guarantees electrical safety and helps in design optimization.We find that there is a critical boundary of thrusts that cannot be exceededdue to fundamental electrical limitations. We compare the power consumption forone tethered quadcopter and two tethered quadcopters and show that for largequadcopters far enough from the anchor point, a two-quadcopter system consumeslesser power. We show that, for a representative use case of firefighting, atethered system with two quadcopters consumes 26% less power than acorresponding system with one quadcopter. Finally, we present experimentsdemonstrating the use of a two-quadcopter tethered system as compared to aone-quadcopter tethered system in a cluttered environment, such as passingthrough a window and grasping an object over an obstacle.

We study the problem of persistent monitoring of a finite number ofinter-connected geographical nodes by a group of heterogeneous mobile agents.We assign to each geographical node a concave and increasing reward functionthat resets to zero after an agent's visit. Then, we design the optimaldispatch policy of which nodes to visit at what time and by what agent byfinding a policy set that maximizes a utility that is defined as the totalreward collected at visit times. We show that this optimization problem isNP-hard and its computational complexity increases exponentially with thenumber of the agents and the length of the mission horizon. By showing that theutility function is a monotone increasing and submodular set function ofagents' policy, we proceed to propose a suboptimal dispatch policy design witha known optimality gap. To reduce the time complexity of constructing thefeasible search set and also to induce robustness to changes in the operationalfactors, we perform our suboptimal policy design in a receding horizon fashion.Then, to compensate for the shortsightedness of the receding horizon approachfor reward distribution beyond the feasible policies of the agents over thereceding horizon, we add a new term to our utility, which provides a measure ofnodal importance beyond the receding horizon's sight. This term gives thepolicy design an intuition to steer the agents towards the nodes with higherrewards on the patrolling graph. Finally, we discuss how our proposed algorithmcan be implemented in a decentralized manner. A simulation study demonstratesour results.

We study the problem of persistent monitoring of a finite number ofinter-connected geographical nodes by a group of heterogeneous mobile agents.We assign to each geographical node a concave and increasing reward functionthat resets to zero after an agent's visit. Then, we design the optimaldispatch policy of which nodes to visit at what time and by what agent byfinding a policy set that maximizes a utility that is defined as the totalreward collected at visit times. We show that this optimization problem isNP-hard and its computational complexity increases exponentially with thenumber of the agents and the length of the mission horizon. By showing that theutility function is a monotone increasing and submodular set function ofagents' policy, we proceed to propose a suboptimal dispatch policy design witha known optimality gap. To reduce the time complexity of constructing thefeasible search set and also to induce robustness to changes in the operationalfactors, we perform our suboptimal policy design in a receding horizon fashion.Then, to compensate for the shortsightedness of the receding horizon approachfor reward distribution beyond the feasible policies of the agents over thereceding horizon, we add a new term to our utility, which provides a measure ofnodal importance beyond the receding horizon's sight. This term gives thepolicy design an intuition to steer the agents towards the nodes with higherrewards on the patrolling graph. Finally, we discuss how our proposed algorithmcan be implemented in a decentralized manner. A simulation study demonstratesour results.

This work addresses task planning under uncertainty for precision agricultureapplications whereby task costs are uncertain and the gain of completing a taskis proportional to resource consumption (such as water consumption in precisionirrigation). The goal is to complete all tasks while prioritizing those thatare more urgent, and subject to diverse budget thresholds and stochastic costsfor tasks. To describe agriculture-related environments that incorporatestochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-ActionPlanning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabledby SAG, and tackles the task planning problem by simultaneously determining theoptimal tasks to perform and an optimal time to exit (i.e. return to a basestation), at run-time. The proposed approach is tested with both simulated dataand real-world experimental datasets collected in a commercial vineyard, inboth single- and multi-robot scenarios. In all cases, NBA-P outperforms otherevaluated methods in terms of return per visited vertex, wasted resourcesresulting from aborted tasks (i.e. when a budget threshold is exceeded), andtotal visited vertices.

In this paper, we study the Orienteering Aisle-graphs Single-access Problem(OASP), a variant of the orienteering problem for a robot moving in a so-calledsingle-access aisle-graph, i.e., a graph consisting of a set of rows that canbe accessed from one side only. Aisle-graphs model, among others, vineyards orwarehouses. Each aisle-graph vertex is associated with a reward that a robotobtains when visits the vertex itself. As the robot's energy is limited, only asubset of vertices can be visited with a fully charged battery. The objectiveis to maximize the total reward collected by the robot with a battery charge.We first propose an optimal algorithm that solves OASP in O(m^2 n^2) time foraisle-graphs with a single access consisting of m rows, each with n vertices.With the goal of designing faster solutions, we propose four greedy sub-optimalalgorithms that run in at most O(mn (m+n)) time. For two of them, we guaranteean approximation ratio of 1/2(1-1/e), where e is the base of the naturallogarithm, on the total reward by exploiting the well-known submodularityproperty. Experimentally, we show that these algorithms collect more than 80%of the optimal reward.

Periodic disinfection of workspaces can reduce SARS-CoV-2 transmission. Inmany buildings periodic disinfection is performed manually; this has severaldisadvantages: it is expensive, limited in the number of times it can be doneover a day, and poses an increased risk to the workers performing the task. Tosolve these problems, we developed an automated decontamination system thatuses ultraviolet C (UVC) radiation for disinfection, coupled with occupancydetection for its safe operation. UVC irradiation is a well-establishedtechnology for the deactivation of a wide range of pathogens. Our proposedsystem can deactivate pathogens both on surfaces and in the air. The couplingwith occupancy detection ensures that occupants are never directly exposed toUVC lights and their potential harmful effects. To help the wider community, wehave shared our complete work as an open-source repository, to be used underGPL v3.

Energy sources such as batteries do not decrease in mass after consumption, unlike combustion-based fuels. We present the concept of staging energy sources, i.e. consuming energy in stages and ejecting used stages, to progressively reduce the mass of aerial vehicles in-flight which reduces power consumption, and consequently increases flight time. A flight time vs. energy storage mass analysis is presented to show the endurance benefit of staging to multirotors. We consider two specific problems in discrete staging - optimal order of staging given a certain number of energy sources, and optimal partitioning of a given energy storage mass budget into a given number of stages. We then derive results for a continuously staged case of an internal combustion engine driving propellers. Notably, we show that a multirotor powered by internal combustion has an upper limit on achievable flight time independent of the available fuel mass. Lastly, we validate the analysis with flight experiments on a custom two-stage battery-powered quadcopter. This quadcopter can eject a battery stage after consumption in-flight using a custom-designed mechanism, and continue hovering using the next stage. The experimental flight times match well with those predicted from the analysis for our vehicle. We achieve a 19% increase in flight time using the batteries in two stages as compared to a single stage.

This work addresses task planning under uncertainty for precision agricultureapplications whereby task costs are uncertain and the gain of completing a taskis proportional to resource consumption (such as water consumption in precisionirrigation). The goal is to complete all tasks while prioritizing those thatare more urgent, and subject to diverse budget thresholds and stochastic costsfor tasks. To describe agriculture-related environments that incorporatestochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-ActionPlanning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabledby SAG, and tackles the task planning problem by simultaneously determining theoptimal tasks to perform and an optimal time to exit (i.e. return to a basestation), at run-time. The proposed approach is tested with both simulated dataand real-world experimental datasets collected in a commercial vineyard, inboth single- and multi-robot scenarios. In all cases, NBA-P outperforms otherevaluated methods in terms of return per visited vertex, wasted resourcesresulting from aborted tasks (i.e. when a budget threshold is exceeded), andtotal visited vertices.

Soft robots promise improved safety and capability over rigid robots whendeployed in complex, delicate, and dynamic environments. However, the infinitedegrees of freedom and highly nonlinear dynamics of these systems severelycomplicate their modeling and control. As a step toward addressing this openchallenge, we apply the data-driven, Hankel Dynamic Mode Decomposition (HDMD)with time delay observables to the model identification of a highly inertial,helical soft robotic arm with a high number of underactuated degrees offreedom. The resulting model is linear and hence amenable to control via aLinear Quadratic Regulator (LQR). Using our test bed device, a dynamic,lightweight pneumatic fabric arm with an inertial mass at the tip, we show thatthe combination of HDMD and LQR allows us to command our robot to achievearbitrary poses using only open loop control. We further show that Koopmanspectral analysis gives us a dimensionally reduced basis of modes whichdecreases computational complexity without sacrificing predictive power.

There exists an urgent need for efficient tools in disease surveillance to help model and predict the spread of disease. The transmission of insect-borne diseases poses a serious concern to public health officials and the medical and research community at large. In the modeling of this spread, we face bottlenecks in (1) the frequency at which we are able to sample insect vectors in environments that are prone to propagating disease, (2) manual labor needed to set up and retrieve surveillance devices like traps, and (3) the return time in analyzing insect samples and determining if an infectious disease is spreading in a region. To help address these bottlenecks, we present in this paper the design, fabrication, and testing of a novel automated insect capture module (ICM) or trap that aims to improve the rate of transferring samples collected from the environment via aerial robots. The ICM features an ultraviolet light attractant, passive capture mechanism, panels which can open and close for access to insects, and a small onboard computer for automated operation and data logging. At the same time, the ICM is designed to be accessible; it is small-scale, lightweight and low-cost, and can be integrated with commercially available aerial robots. Indoor and outdoor experimentation validates ICM's feasibility in insect capturing and safe transportation. The device can help bring us one step closer toward achieving fully autonomous and scalable epidemiology by leveraging autonomous robots technology to aid the medical and research community.

This paper analyzes the meeting time between a pair of pursuer and evaderperforming random walks on digraphs. The existing bounds on the meeting timeusually work only for certain classes of walks and cannot be used to formulateoptimization problems and design robotic strategies. First, by analyzingmultiple random walks on a common graph as a single random walk on theKronecker product graph, we provide the first closed-form expression for theexpected meeting time in terms of the transition matrices of the moving agents.This novel expression leads to necessary and sufficient conditions for themeeting time to be finite and to insightful graph-theoretic interpretations.Second, based on the closed-form expression, we setup and study theminimization problem for the expected capture time for a pursuer/evader pair.We report theoretical and numerical results on basic case studies to show theeffectiveness of the design.

In most real world scenarios, a policy trained by reinforcement learning inone environment needs to be deployed in another, potentially quite differentenvironment. However, generalization across different environments is known tobe hard. A natural solution would be to keep training after deployment in thenew environment, but this cannot be done if the new environment offers noreward signal. Our work explores the use of self-supervision to allow thepolicy to continue training after deployment without using any rewards. Whileprevious methods explicitly anticipate changes in the new environment, weassume no prior knowledge of those changes yet still obtain significantimprovements. Empirical evaluations are performed on diverse simulationenvironments from DeepMind Control suite and ViZDoom, as well as real roboticmanipulation tasks in continuously changing environments, taking observationsfrom an uncalibrated camera. Our method improves generalization in 31 out of 36environments across various tasks and outperforms domain randomization on amajority of environments.

In this paper, we consider the problem of creating a safe-by-design RectifiedLinear Unit (ReLU) Neural Network (NN), which, when composed with an arbitrarycontrol NN, makes the composition provably safe. In particular, we propose analgorithm to synthesize such NN filters that safely correct control inputsgenerated for the continuous-time Kinematic Bicycle Model (KBM). ShieldNNcontains two main novel contributions: first, it is based on a novel BarrierFunction (BF) for the KBM model; and second, it is itself a provably soundalgorithm that leverages this BF to a design a safety filter NN with safetyguarantees. Moreover, since the KBM is known to well approximate the dynamicsof four-wheeled vehicles, we show the efficacy of ShieldNN filters in CARLAsimulations of four-wheeled vehicles. In particular, we examined the effect ofShieldNN filters on Deep Reinforcement Learning trained controllers in thepresence of individual pedestrian obstacles. The safety properties of ShieldNNwere borne out in our experiments: the ShieldNN filter reduced the number ofobstacle collisions by 99.4%-100%. Furthermore, we also studied the effect ofincorporating ShieldNN during training: for a constant number of episodes, 28%less reward was observed when ShieldNN wasn't used during training. Thissuggests that ShieldNN has the further property of improving sample efficiencyduring RL training.

Conformable robotic systems are attractive for applications in which they may actuate structures with large surface areas, provide forces through wearable garments, or enable autonomous robotic systems. We present a new family of soft actuators that we refer to as Fluidic Fabric Muscle Sheets (FFMS). They are composite fabric structures that integrate fluidic transmissions based on arrays of elastic tubes. These sheet-like actuators can strain, squeeze, bend, and conform to hard or soft objects of arbitrary shapes or sizes, including the human body. We show how to design and fabricate FFMS actuators via facile apparel engineering methods, including computerized sewing techniques that determine the stress and strain distributions that can be generated. We present a simple mathematical model that proves effective for predicting their performance. FFMS can operate at frequencies of 5 Hz or more, achieve engineering strains exceeding 100%, and exert forces >115 times their weight. They can be safely used in intimate contact with the human body even when delivering stresses exceeding 106 Pascals. We demonstrate their versatility for actuating a variety of bodies or structures, and in configurations that perform multiaxis actuation, including bending and shape change. As we also show, FFMS can be used to exert forces on body tissues for wearable and biomedical applications. We demonstrate several potential use cases, including a miniature steerable robot, a glove for grasp assistance, garments for applying compression to the extremities, and devices for actuating small body regions or tissues via localized skin stretch.

Conformable robotic systems are attractive for applications in which they may actuate structures with large surface areas, provide forces through wearable garments, or enable autonomous robotic systems. We present a new family of soft actuators that we refer to as Fluidic Fabric Muscle Sheets (FFMS). They are composite fabric structures that integrate fluidic transmissions based on arrays of elastic tubes. These sheet-like actuators can strain, squeeze, bend, and conform to hard or soft objects of arbitrary shapes or sizes, including the human body. We show how to design and fabricate FFMS actuators via facile apparel engineering methods, including computerized sewing techniques that determine the stress and strain distributions that can be generated. We present a simple mathematical model that proves effective for predicting their performance. FFMS can operate at frequencies of 5 Hz or more, achieve engineering strains exceeding 100%, and exert forces >115 times their weight. They can be safely used in intimate contact with the human body even when delivering stresses exceeding 106 Pascals. We demonstrate their versatility for actuating a variety of bodies or structures, and in configurations that perform multiaxis actuation, including bending and shape change. As we also show, FFMS can be used to exert forces on body tissues for wearable and biomedical applications. We demonstrate several potential use cases, including a miniature steerable robot, a glove for grasp assistance, garments for applying compression to the extremities, and devices for actuating small body regions or tissues via localized skin stretch.

This paper presents a round-trip strategy of multirotors subject to unknownflow disturbances. During the outbound flight, the vehicle immediately utilizesthe wind disturbance estimations in feedback control, as an attempt to reducethe tracking error. During this phase, the disturbance estimations with respectto the position are also recorded for future use. For the return flight, thedisturbances previously collected are then routed through a feedforwardcontroller. The major assumption here is that the disturbances may vary overspace, but not over time during the same mission. We demonstrate theeffectiveness of this feedforward strategy via experiments with two differenttypes of wind flows; a simple jet flow and a more complex flow. To use as abaseline case, a cascaded PD controller with an additional feedback loop fordisturbance estimation was employed for outbound flights. To display ourcontributions regarding the additional feedforward approach, an additionalfeedforward correction term obtained via prerecorded data was integrated forthe return flight. Compared to the baseline controller, the feedforwardcontroller was observed to produce 43% less RMSE position error at a vehicleground velocity of 1 m/s with 6 m/s of environmental wind velocity. Thisfeedforward approach also produced 14% less RMSE position error for the complexflows as well.

In many settings, such as robotics, demonstrations provide a natural way to specify tasks. However, most methods for learning from demonstrations either do not provide guarantees that the learned artifacts can be safely composed or do not explicitly capture temporal properties. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naïve algorithm with running time exponential in the episode length, into a polynomial time algorithm.

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.

This paper analyzes the meeting time between a pair of pursuer and evaderperforming random walks on digraphs. The existing bounds on the meeting timeusually work only for certain classes of walks and cannot be used to formulateoptimization problems and design robotic strategies. First, by analyzingmultiple random walks on a common graph as a single random walk on theKronecker product graph, we provide the first closed-form expression for theexpected meeting time in terms of the transition matrices of the moving agents.This novel expression leads to necessary and sufficient conditions for themeeting time to be finite and to insightful graph-theoretic interpretations.Second, based on the closed-form expression, we setup and study theminimization problem for the expected capture time for a pursuer/evader pair.We report theoretical and numerical results on basic case studies to show theeffectiveness of the design.

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.

In many settings, such as robotics, demonstrations provide a natural way to specify tasks. However, most methods for learning from demonstrations either do not provide guarantees that the learned artifacts can be safely composed or do not explicitly capture temporal properties. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naïve algorithm with running time exponential in the episode length, into a polynomial time algorithm.

We report two decentralized multi-agent cooperative localization algorithmsin which, to reduce the communication cost, inter-agent state estimatecorrelations are not maintained but accounted for implicitly. In our firstalgorithm, to guarantee filter consistency, we account for unknown inter-agentcorrelations via an upper bound on the joint covariance matrix of the agents.In the second method, we use an optimization framework to estimate the unknowninter-agent cross-covariance matrix. In our algorithms, each agent localizesitself in a global coordinate frame using a local filter driven by local deadreckoning and occasional absolute measurement updates, and opportunisticallycorrects its pose estimate whenever it can obtain relative measurements withrespect to other mobile agents. To process any relative measurement, only theagent taken the measurement and the agent the measurement is taken from need tocommunicate with each other. Consequently, our algorithms are decentralizedalgorithms that do not impose restrictive network-wide connectivity condition.Moreover, we make no assumptions about the type of agents or relativemeasurements. We demonstrate our algorithms in simulation and arobotic~experiment.

In this paper, we consider the Constant-cost Orienteering Problem (COP) wherea robot, constrained by a limited travel budget, aims at selecting a path withthe largest reward in an aisle-graph. The aisle-graph consists of a set ofloosely connected rows where the robot can change lane only at either end, butnot in the middle. Even when considering this special type of graphs, theorienteering problem is known to be NP-hard. We optimally solve in polynomialtime two special cases, COP-FR where the robot can only traverse full rows, andCOP-SC where the robot can access the rows only from one side. To solve thegeneral COP, we then apply our special case algorithms as well as a newheuristic that suitably combines them. Despite its light computationalcomplexity and being confined into a very limited class of paths, the optimalsolutions for COP-FR turn out to be competitive even for COP in both real andsynthetic scenarios. Furthermore, our new heuristic for the general caseoutperforms state-of-art algorithms, especially for input with highlyunbalanced rewards.

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third-party off-The-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-Time certification of correct operation. To address these challenges, we present SOTER, a robotics programming framework with two key components: (1) a programming language for implementing and testing high-level reactive robotics software, and (2) an integrated runtime assurance (RTA) system that helps enable the use of uncertified components, while still providing safety guarantees. SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification. The framework provides a formal guarantee that a well-formed RTA module always satisfies the safety specification, without completely sacrificing performance by using higher performance uncertified components whenever safe. SOTER allows the complex robotics software stack to be constructed as a composition of RTA modules, where each uncertified component is protected using a RTA module. To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that the SOTER-enabled RTA ensures the safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior.

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third-party off-The-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-Time certification of correct operation. To address these challenges, we present SOTER, a robotics programming framework with two key components: (1) a programming language for implementing and testing high-level reactive robotics software, and (2) an integrated runtime assurance (RTA) system that helps enable the use of uncertified components, while still providing safety guarantees. SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification. The framework provides a formal guarantee that a well-formed RTA module always satisfies the safety specification, without completely sacrificing performance by using higher performance uncertified components whenever safe. SOTER allows the complex robotics software stack to be constructed as a composition of RTA modules, where each uncertified component is protected using a RTA module. To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that the SOTER-enabled RTA ensures the safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior.

We consider the problem of extracting safe environments and controllers for reach-avoid objectives for systems with known state and control spaces, but unknown dynamics. In a given environment, a common approach is to synthesize a controller from an abstraction or a model of the system (potentially learned from data). However, in many situations, the relationship between the dynamics of the model and the actual system is not known; and hence it is difficult to provide safety guarantees for the system. In such cases, the Standard Simulation Metric (SSM), defined as the worst-case norm distance between the model and the system output trajectories, can be used to modify a reach-avoid specification for the system into a more stringent specification for the abstraction. Nevertheless, the obtained distance, and hence the modified specification, can be quite conservative. This limits the set of environments for which a safe controller can be obtained. We propose SPEC, a specification-centric simulation metric, which overcomes these limitations by computing the distance using only the trajectories that violate the specification for the system. We show that modifying a reach-avoid specification with SPEC allows us to synthesize a safe controller for a larger set of environments compared to SSM. We also propose a probabilistic method to compute SPEC for a general class of systems. Case studies using simulators for quadrotors and autonomous cars illustrate the advantages of the proposed metric for determining safe environment sets and controllers.

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial states such that a robot trajectory starting from these initial states is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of safe initial states. To mathematically model the imaging function, that maps the robot position to the LiDAR image, we introduce the notion of imaging-adapted partitions of the workspace in which the imaging function is guaranteed to be affine. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) non-linearity, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space of feasible ReLU assignments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with the increasing complexity of the neural network controller.

We report two decentralized multi-agent cooperative localization algorithmsin which, to reduce the communication cost, inter-agent state estimatecorrelations are not maintained but accounted for implicitly. In our firstalgorithm, to guarantee filter consistency, we account for unknown inter-agentcorrelations via an upper bound on the joint covariance matrix of the agents.In the second method, we use an optimization framework to estimate the unknowninter-agent cross-covariance matrix. In our algorithms, each agent localizesitself in a global coordinate frame using a local filter driven by local deadreckoning and occasional absolute measurement updates, and opportunisticallycorrects its pose estimate whenever it can obtain relative measurements withrespect to other mobile agents. To process any relative measurement, only theagent taken the measurement and the agent the measurement is taken from need tocommunicate with each other. Consequently, our algorithms are decentralizedalgorithms that do not impose restrictive network-wide connectivity condition.Moreover, we make no assumptions about the type of agents or relativemeasurements. We demonstrate our algorithms in simulation and arobotic~experiment.

This paper considers the problem of cooperative localization (CL) usinginter-robot measurements for a group of networked robots with limited on-boardresources. We propose a novel recursive algorithm in which each robot localizesitself in a global coordinate frame by local dead reckoning, andopportunistically corrects its pose estimate whenever it receives a relativemeasurement update message from a server. The computation and storage cost perrobot in terms of the size of the team is of order O(1), and the robots areonly required to transmit information when they are involved in a relativemeasurement. The server also only needs to compute and transmit update messageswhen it receives an inter-robot measurement. We show that under perfectcommunication, our algorithm is an alternative but exact implementation of ajoint CL for the entire team via Extended Kalman Filter (EKF). The perfectcommunication however is not a hard requirement. In fact, we show that ouralgorithm is intrinsically robust with respect to communication failures, withformal guarantees that the updated estimates of the robots receiving the updatemessage are of minimum variance in a first-order approximate sense at thatgiven timestep. We demonstrate the performance of the algorithm in simulationand experiments.

We consider the problem of cooperative localization (CL) via interrobot measurements for a team of networked robots with limited on-board resources. We propose a novel algorithm in which each robot localizes itself in a global coordinate frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only transmitting information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an interrobot measurement. Under perfect communication, our algorithm is an alternative implementation of a joint CL for the team via an extended Kalman filter. However, perfect communication is not a hard constraint. We show that our algorithm is robust to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of our algorithm in simulation and experiments.

In this paper, we focus on activating only a few sensors, among manyavailable, to estimate the state of a stochastic process of interest. Thisproblem is important in applications such as target tracking and simultaneouslocalization and mapping (SLAM). It is challenging since it involves stochasticsystems whose evolution is largely unknown, sensors with nonlinearmeasurements, and limited operational resources that constrain the number ofactive sensors at each measurement step. We provide an algorithm applicable togeneral stochastic processes and nonlinear measurements whose time complexityis linear in the planning horizon and whose performance is a multiplicativefactor 1/2 away from the optimal performance. This is notable because thealgorithm offers a significant computational advantage over the polynomial-timealgorithm that achieves the best approximation factor 1/e. In addition, forimportant classes of Gaussian processes and nonlinear measurements corruptedwith Gaussian noise, our algorithm enjoys the same time complexity as even thestate-of-the-art algorithms for linear systems and measurements. We achieve ourresults by proving two properties for the entropy of the batch state vectorconditioned on the measurements: a) it is supermodular in the choice of thesensors; b) it has a sparsity pattern (involves block tri-diagonal matrices)that facilitates its evaluation at each sensor set.

In this paper, we focus on activating only a few sensors, among manyavailable, to estimate the state of a stochastic process of interest. Thisproblem is important in applications such as target tracking and simultaneouslocalization and mapping (SLAM). It is challenging since it involves stochasticsystems whose evolution is largely unknown, sensors with nonlinearmeasurements, and limited operational resources that constrain the number ofactive sensors at each measurement step. We provide an algorithm applicable togeneral stochastic processes and nonlinear measurements whose time complexityis linear in the planning horizon and whose performance is a multiplicativefactor 1/2 away from the optimal performance. This is notable because thealgorithm offers a significant computational advantage over the polynomial-timealgorithm that achieves the best approximation factor 1/e. In addition, forimportant classes of Gaussian processes and nonlinear measurements corruptedwith Gaussian noise, our algorithm enjoys the same time complexity as even thestate-of-the-art algorithms for linear systems and measurements. We achieve ourresults by proving two properties for the entropy of the batch state vectorconditioned on the measurements: a) it is supermodular in the choice of thesensors; b) it has a sparsity pattern (involves block tri-diagonal matrices)that facilitates its evaluation at each sensor set.

The migration of robots from the laboratory into sensitive home settings as commercially available therapeutic agents represents a significant transition for information privacy and ethical imperatives. We present new privacy paradigms and apply the fair information practices (FIPs) to investigate concerns unique to the placement of therapeutic robots in private home contexts. We then explore the importance and utility of research ethics as operationalized by existing human subjects research frameworks to guide the consideration of therapeutic robotic users—a step vital to the continued research and development of these platforms. Together, privacy and research ethics frameworks provide two complementary approaches to protect users and ensure responsible yet robust information sharing for technology development. We make recommendations for the implementation of these principles—paying particular attention to specific principles that apply to vulnerable individuals (i.e., children, disabled, or elderly persons)—to promote the adoption and continued improvement of long-term, responsible, and research-enabled robotics in private settings.

The migration of robots from the laboratory into sensitive home settings as commercially available therapeutic agents represents a significant transition for information privacy and ethical imperatives. We present new privacy paradigms and apply the fair information practices (FIPs) to investigate concerns unique to the placement of therapeutic robots in private home contexts. We then explore the importance and utility of research ethics as operationalized by existing human subjects research frameworks to guide the consideration of therapeutic robotic users—a step vital to the continued research and development of these platforms. Together, privacy and research ethics frameworks provide two complementary approaches to protect users and ensure responsible yet robust information sharing for technology development. We make recommendations for the implementation of these principles—paying particular attention to specific principles that apply to vulnerable individuals (i.e., children, disabled, or elderly persons)—to promote the adoption and continued improvement of long-term, responsible, and research-enabled robotics in private settings.

This technical report presents an introduction to different aspects ofmulti-fingered robot grasping. After having introduced relevant mathematicalbackground for modeling, form and force closure are discussed. Next, we presentan overview of various grasp planning algorithms with the objective ofillustrating different approaches to solve this problem. Finally, we discussgrasp performance benchmarking.

We consider cooperative localization technique for mobile agents withcommunication and computation capabilities. We start by provide and overview ofdifferent decentralization strategies in the literature, with special focus onhow these algorithms maintain an account of intrinsic correlations betweenstate estimate of team members. Then, we present a novel decentralizedcooperative localization algorithm that is a decentralized implementation of acentralized Extended Kalman Filter for cooperative localization. In thisalgorithm, instead of propagating cross-covariance terms, each agent propagatesnew intermediate local variables that can be used in an update stage to createthe required propagated cross-covariance terms. Whenever there is a relativemeasurement in the network, the algorithm declares the agent making thismeasurement as the interim master. By acquiring information from the interimlandmark, the agent the relative measurement is taken from, the interim mastercan calculate and broadcast a set of intermediate variables which each robotcan then use to update its estimates to match that of a centralized ExtendedKalman Filter for cooperative localization. Once an update is done, no furthercommunication is needed until the next relative measurement.

Time synchronization has been studied extensively over the recent years with an advent of time critical applications for wireless sensor networks. The distribution of the global reference time over the radio links is the most popular synchronization mechanism. Often overlooked, the timing uncertainties spread across transmitter to the receiver, limit the accuracy of state-of-the-art synchronization protocols. These timing uncertainties are due to the instability of crystal oscillators and timestamping mechanisms. The effect of these uncertainties is cumulative in nature and build up the synchronization error. On the other hand, limited resources of energy, computational units, storage, and bandwidth are a driving force towards lightweight protocols. Hence, this paper presents a deep analysis of each source of timing uncertainty and motivates uncertainty-aware time synchronization. Extensive experiments are conducted to highlight the contribution of each source and provide recommendations for mitigating the resultant timing uncertainty. We have also proposed a Lightweight Kalman filter based on our analysis to meet the limited resources constraint.

Time synchronization has been studied extensively over the recent years with an advent of time critical applications for wireless sensor networks. The distribution of the global reference time over the radio links is the most popular synchronization mechanism. Often overlooked, the timing uncertainties spread across transmitter to the receiver, limit the accuracy of state-of-the-art synchronization protocols. These timing uncertainties are due to the instability of crystal oscillators and timestamping mechanisms. The effect of these uncertainties is cumulative in nature and build up the synchronization error. On the other hand, limited resources of energy, computational units, storage, and bandwidth are a driving force towards lightweight protocols. Hence, this paper presents a deep analysis of each source of timing uncertainty and motivates uncertainty-aware time synchronization. Extensive experiments are conducted to highlight the contribution of each source and provide recommendations for mitigating the resultant timing uncertainty. We have also proposed a Lightweight Kalman filter based on our analysis to meet the limited resources constraint.

We consider cooperative localization technique for mobile agents withcommunication and computation capabilities. We start by provide and overview ofdifferent decentralization strategies in the literature, with special focus onhow these algorithms maintain an account of intrinsic correlations betweenstate estimate of team members. Then, we present a novel decentralizedcooperative localization algorithm that is a decentralized implementation of acentralized Extended Kalman Filter for cooperative localization. In thisalgorithm, instead of propagating cross-covariance terms, each agent propagatesnew intermediate local variables that can be used in an update stage to createthe required propagated cross-covariance terms. Whenever there is a relativemeasurement in the network, the algorithm declares the agent making thismeasurement as the interim master. By acquiring information from the interimlandmark, the agent the relative measurement is taken from, the interim mastercan calculate and broadcast a set of intermediate variables which each robotcan then use to update its estimates to match that of a centralized ExtendedKalman Filter for cooperative localization. Once an update is done, no furthercommunication is needed until the next relative measurement.

In this paper, we consider a stochastic deployment problem, where a robotic swarm is tasked with the objective of positioning at least one robot at each of a set of pre-assigned targets while meeting a temporal deadline. Travel times and failure rates are stochastic but related, inasmuch as failure rates increase with speed. To maximize chances of success while meeting the deadline, a control strategy has therefore to balance safety and performance. Our approach is to cast the problem within the theory of constrained Markov decision processes (CMDPs), whereby we seek to compute policies that maximize the probability of successful deployment while ensuring that the expected duration of the task is bounded by a given deadline. To account for uncertainties in the problem parameters, we consider a robust formulation and we propose efficient solution algorithms, which are of independent interest. Numerical experiments confirming our theoretical results are presented and discussed.

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal.

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal.

In this paper, we consider a stochastic deployment problem, where a robotic swarm is tasked with the objective of positioning at least one robot at each of a set of pre-assigned targets while meeting a temporal deadline. Travel times and failure rates are stochastic but related, inasmuch as failure rates increase with speed. To maximize chances of success while meeting the deadline, a control strategy has therefore to balance safety and performance. Our approach is to cast the problem within the theory of constrained Markov decision processes (CMDPs), whereby we seek to compute policies that maximize the probability of successful deployment while ensuring that the expected duration of the task is bounded by a given deadline. To account for uncertainties in the problem parameters, we consider a robust formulation and we propose efficient solution algorithms, which are of independent interest. Numerical experiments confirming our theoretical results are presented and discussed.

We present a framework for designing cheap control architectures of embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent's embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments indicate that the controller complexity predicted by our theory is close to the minimal sufficient value, which means that the theory has direct practical implications.

This paper addresses the problem of collaborative tracking of dynamic targetsin wireless sensor networks. A novel distributed linear estimator, which is aversion of a distributed Kalman filter, is derived. We prove that the filter ismean square consistent in the case of static target estimation. When largesensor networks are deployed, it is common that the sensors do not have goodknowledge of their locations, which affects the target estimation procedure.Unlike most existing approaches for target tracking, we investigate theperformance of our filter when the sensor poses need to be estimated by anauxiliary localization procedure. The sensors are localized via a distributedJacobi algorithm from noisy relative measurements. We prove strong convergenceguarantees for the localization method and in turn for the joint localizationand target estimation approach. The performance of our algorithms isdemonstrated in simulation on environmental monitoring and target trackingtasks.

This paper addresses the problem of collaborative tracking of dynamic targetsin wireless sensor networks. A novel distributed linear estimator, which is aversion of a distributed Kalman filter, is derived. We prove that the filter ismean square consistent in the case of static target estimation. When largesensor networks are deployed, it is common that the sensors do not have goodknowledge of their locations, which affects the target estimation procedure.Unlike most existing approaches for target tracking, we investigate theperformance of our filter when the sensor poses need to be estimated by anauxiliary localization procedure. The sensors are localized via a distributedJacobi algorithm from noisy relative measurements. We prove strong convergenceguarantees for the localization method and in turn for the joint localizationand target estimation approach. The performance of our algorithms isdemonstrated in simulation on environmental monitoring and target trackingtasks.