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In this article, we address the problem of coordinating the motion of a team of robots with limited field of view (FOV), which induces asymmetry in their interactions. In this context, we first propose a general coordinated motion framework for multirobot systems with triangular FOV capable of guaranteeing stability under asymmetric (directed) interactions. In deriving this framework, we illustrate that asymmetry in multirobot interactions can lead to degenerate configurations for which a fully decentralized controller may be insufficient to achieve coordination. Thus, we introduce a switching control mechanism that achieves adaptive decentralization, enabling collaborative behaviors that seek support of a centralized planner for situations that are inherently unstable (degenerate). To demonstrate the generality of our framework we provide a case study involving varying team objectives, such as topology control, that the robots can achieve with limited FOV, while remaining stable. Experimental and numerical validations based on the previously discussed case study are provided to corroborate the theoretical findings [ABSTRACT FROM AUTHOR]

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient. Note to Practitioners—This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications. [ABSTRACT FROM AUTHOR]

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of (r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article presents a multi-agent control architecture and an online optimization method based on a dynamic average consensus to coordinate the power consumption of a large population of thermostatically controlled loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile, and enable demand-side management. The proposed architecture and methods exploit only local measurements of power consumption via smart power sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited, and agents preserve their privacy by cooperating anonymously only through consensus-based distributed estimation. The interactions among devices occur through an unstructured peer-to-peer (P2P) network over the Internet. Methods for parameter identification, state estimation, and mixed logical modeling of TCLs and SPSs are included. The architecture is designed from a multi-agent and plug-and-play perspective, in which existing household appliances can interact with each other in an urban environment. Finally, a novel low-cost testbed is proposed along with numerical tests and experimental validation. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of general topology control in multirobot systems with nonholonomic kinematics. Our contribution is twofold: We first demonstrate the correctness of topology control under the assumption that the network topology can switch arbitrarily and that potential-based mobility is discontinuous with respect to topology changes; we then demonstrate that a multirobot team under the above listed conditions continues to achieve topology control when actuator saturation is applied and in the presence of arbitrary discontinuous (and possibly nonpairwise) exogenous objectives. Simulation results are given to corroborate our theoretical findings. [ABSTRACT FROM PUBLISHER]

In this paper, we address the connectivity maintenance problem for a multirobot system that moves according to a given bounded collective control objective. We assume that the interaction among the robotic units is limited by a given visibility radius both in terms of sensing and communication capabilities. For this scenario, we propose a decentralized bounded control law that can provably preserve the connectivity of the multirobot system over time. We characterize the effect of the connectivity control term on the achievement of the collective control objective by resorting to an input-to-state stability-like analysis. We provide numerical and experimental results to corroborate the theoretical findings and assess the effectiveness of the proposed bounded connectivity maintenance control law. [ABSTRACT FROM PUBLISHER]

We formulate the problem of robots ferrying messages between statically-placed source and sink pairs that they can communicate with wirelessly. We first analyze the capacity region for this problem under ideal conditions. We indicate how robots could be scheduled optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival rate. We then consider the setting where the arrival rate is unknown and present a coarse-grained backpressure message ferrying algorithm (CBMF) for it. In CBMF, the robots are matched to sources and sinks once every epoch to maximize a queue-differential-based weight. The matching controls both motion and transmission for each robot. We show through analysis and simulations the conditions under which CBMF can stabilize the network, and its corresponding delay performance. From a practical point of view, we propose a heuristic approach to adapt the epoch duration according to network conditions that can improve the end-to-end delay while guaranteeing the network stability at the same time. We also study the structural properties with its explicit delay performance of the CBMF algorithm in a homogeneous network. [ABSTRACT FROM PUBLISHER]

Motivated by the strong influence network rigidity has on collaborative systems, in this paper, we consider the problem of partitioning a multiagent network into two sub-teams, a bipartition, such that the resulting sub-teams are topologically rigid. In this direction, we determine the existence conditions for rigidity-preserving bipartitions, and provide an iterative algorithm that identifies such partitions in polynomial time. In particular, the relationship between rigid graph partitions and the previously identified Z-link edge structure is given, yielding a feasible direction for graph search. Adapting a supergraph search mechanism, we then detail a methodology for discerning graphs cuts that represent valid rigid bipartitions. Next, we extend our methods to a decentralized context by exploiting leader election and an improved graph search to evaluate feasible cuts using only local agent-to-agent communication. Finally, full algorithm details and pseudocode are provided, together with simulation results that verify correctness and demonstrate complexity. [ABSTRACT FROM PUBLISHER]

In this paper, we address the decentralized and parallel construction of rigid graphs in the plane that optimize an edge-weighted objective function under cardinality constraints. Two auction-based algorithms to solve this problem in a decentralized fashion are first proposed. Centered around the notion of leader election, the first approach finds an optimal solution through a greedy bidding, while the second approach provides a sub-optimal solution which reduces complexity according to a sliding mode parameter. Then, by exploiting certain local structural properties of graph rigidity, a parallelization to build a portion of the optimal solution in constant time is derived. A theoretical characterization of algorithm performance is provided together with complexity analysis. Finally, simulation results are presented to corroborate the theoretical findings. [ABSTRACT FROM PUBLISHER]

This technical communique represents a generalization of the convergence analysis for the consensus algorithm proposed in Priolo et al. (2014). Although the consensus was reached for any strongly connected weighted digraphs (SCWD), the convergence analysis provided in Priolo et al. (2014) was only valid for diagonalizable matrices encoding a SCWD. The result we present here generalizes the previous one to all possible matrices encoding a SCWD that can be used in the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a decentralized model and control framework for the assignment and execution of tasks, i.e. the dynamic task planning, for a network of heterogeneous robots. The proposed modeling framework allows the design of missions, defined as sets of tasks, in order to achieve global objectives regardless of the actual characteristics of the robotic network. The concept of skills, defined by the mission designer and considered as constraints for the mission execution, is exploited to distribute tasks across the robotic network. In addition, we develop a decentralized control algorithm, based on the concept of skills for decoupling the mission design from its deployment, which combines task assignment and execution through a consensus-based approach. Finally, conditions upon which the proposed decentralized formulation is equivalent to a centralized one are discussed. Experimental results are provided to validate the effectiveness of the proposed framework in a real-world scenario. [ABSTRACT FROM AUTHOR]