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In this paper, we have designed a region reaching controller for an Autonomous Underwater Vehicle(AUV) using potential energy concept. The proposed controller also ensures static obstacle avoidance with obstacles considered as repulsive region. A filtering technique is utilized to produce an auxiliary velocity signal and thus, eliminate the requirement of velocity measurement. The control strategy provides asymptotic regulation of AUV pose. A simulation study of the proposed controller on ODIN AUV is also presented.

This paper presents a novel control architecture for operational space control when the end effector or the robotic chain is kinematically constrained. Particularly, we address kinematic control of robots operating in the presence of obstacles such as point, plane, or barrier constraints imposed on a point on the manipulator. The main advantage of the proposed approach is that we are able to control the end-effector motion in the normal way using conventional operational space control schemes, and by re-writing the Jacobian matrix we also guarantee that the constraints are satisfied. The most challenging problem of obstacle avoidance of robotic manipulators is the extremely complex structure that arises when the obstacles are mapped from the operational space to joint space. We solve this by first finding a new set of velocity variables for a point on the robot in the vicinity of the obstacle, and on these new variables we impose a structure which guarantees that the robot does not hit the obstacle. We then find a mapping denoted the Constrained Jacobian Matrix from the joint variables to these new velocity variables and use this mapping to find a trajectory in joint space for which the constraints are not violated. We present for the first time the Constrained Jacobian Matrix which imposes a kinematic constraint on the manipulator chain and show the efficiency of the approach through experiments on a real robot.

This paper presents a new approach for the energy-efficient trajectory planning of a mobile agent with obstacle avoidance. The motion of the mobile agent is subject to position constraints characterizing an obstacle (keep-out region) as well as velocity, acceleration, and control constraints. The original optimal control problem is transformed into a mathematical programming problem where the obstacle is described by a set of linear constraints and switching times, which specify the sequence of active constraints corresponding to the obstacle. A two-stages decomposition method is proposed to solve the optimal control inputs and switch times and is verified through simulations. The proposed approach can be applied to solve general obstacle avoidance trajectory planning problems.

This paper deals with the problem of active target tracking with obstacle avoidance for multi-robot systems. A nonlinear model predictive formation control is presented which uses potential functions as terms of the cost function. These terms penalize the proximity with mates and obstacles, splitting the problem of obstacle avoidance into two repulse functions. Experimental results with real robots are presented to demonstrate the performance of the approach.

We are motivated by the idea of finding feedback laws for rotations that only require transmission of scalar signals. In particular, we develop an extremum seeking control law for systems living on the special orthogonal group. In addition, we equip our system with the capability of obstacle avoidance using the idea of navigation functions, along the lines of Koditschek and Rimon [1990].

In this work, we propose a solution to the inverse kinematics problem based on the differential kinematics and on a recently proposed algorithm which estimates the inverse of the Jacobian matrix dynamically. The output of the algorithm can be interpreted as a filtered inverse (FI) of the Jacobian matrix. An interesting property of the FI algorithm is its ability to cope with kinematic singularities. The update law of the estimator is driven by error signals that consider both the left and the right inverse matrices, thus enabling trajectory tracking and minimization of the control effort simultaneously. This paper shows that the FI algorithm can be applied to a Jacobian matrix augmented with additional constraints, which allows for setting the priority or weight to different control objectives, such as obstacle avoidance. Simulation results are presented to illustrate the performance and feasibility of the proposed solution.

This paper describes path planning and control of an autonomous power distribution system. The aim is to study the use of the autonomous mobile power-grid systems after disasters to accelerate search, rescue, and recovery efforts. The concept is demonstrated through an autonomous electrical cabling and connection mission between a power source and a power load in a cluttered environment using lab-size platforms. The developed system will be scalable to real-size. The ultimate goal of this work is developing intelligent power electronics and a distributed autonomous mobile microgrid. It will be capable of regulating power flow at a desired voltage and frequency level, meeting load demands and adaptable to changes in situation, power demands, or generations.

A model predictive control based algorithm for maintenance of leader-follower formations of micro-scale aerial vehicles is proposed in this paper. The approach is designed for stabilization of teams of unmanned quadrotor helicopters and for their motion planning into a distant target region. The presented method of the model predictive control with a planning horizon enables integration of an obstacle avoidance function into the local control of the formation as well as into the global plan of formation movement. Deployment of the method in real-world scenarios, with particular interest in failure recovery and inter-vehicle avoidance, is verified in various simulations.

This paper presents a novel obstacle avoidance approach that is capable of dealing with both static and dynamic obstacles in the environment with guaranteed collision-free navigation for haptic teleoperation of VTOL aerial robots. The proposed approach modulates the set point for the vehicle's controller based on the user input energy, estimated potential energy and vehicle's kinetic energy. By shuffling the potential and kinetic energy, vehicle's velocity is regulated according to the permissible kinetic energy and thus obstacle avoidance is achieved. With careful design of the potential field, this approach offers a guaranteed collision-free navigation with the presence of both stationary and moving obstacles. Incorporating the novel approach with the Dynamic Kinesthetic Boundary, the human operator can better perceive the environment where the robot is deployed through the rich spatial haptic cues rather than an onset gradual single force vector. Analysis is provided and proves that in the case of perfect velocity tracking of the slave system, the proposed algorithm can guarantee a collision-free navigation through the environment. Simulations and experiments were conducted, and the results provide verification of the effectiveness of the proposed approach in obstacle and collision avoidance for haptic teleoperation of aerial robots.

Autonomous mobile robot navigation using only local sensory data in an unstructured environment poses a challenge for mobile robots. Our approach aims to evolve a single neural controller that simultaneously learns obstacle avoidance and target seeking without an explicit behavior switching scheme unlike some other recent approaches. We train the robot in a static, unmapped environment comprising of multiple obstacles of different shapes and sizes. We study 4 different training schemes that emphasize the learning objectives differently. The trained controller is validated by simulations across a diverse range of environments different from the training environment. A comparative study of the robot behavior under different objective functions has been done. We discuss the performance measured on the basis of several metrics.

Path planning for urban environment with obstacle is considered. Candidate Γ and S shaped curves are derived using Four Parameter Logistic curves. Two basic obstacle shapes are chosen and closed form analytic conditions are derived to avoid them. Simple analytic conditions for the heading and the curvature at the start and end points are derived for generating Γ and S shaped curves, respectively. Further, using the analytic curvature continuity condition Γ and S shaped curves can be joined (as Γ – S or S – S) to generate feasible paths with intermediate waypoints. Simulation studies are carried out to generate multiple feasible paths in dense obstacle environment.

This paper presents the development of the C-Enduro unmanned maritime surface vehicle (USV) and the collision avoidance algorithms for the sense and avoid system. The USV is designed to operate at sea for extended periods of time (up to 3-months) but also have the capability to transit for a short range at high speeds. The collision avoidance algorithms which also take COLREGS rules into account were validated in simulation.

This paper discusses how to generate mobile robots’ behaviors using genetic algorithms (GA). The behaviors are built using state machines implemented in recurrent neural networks (RNN), controlling the movements of a humanoid mobile robot. The weights of the RNN are found using a GA, these are evaluated according to a fitness function that grades their performance. Basically, this function evaluates the robot's performance when it goes from an origin to a destination, and the grading of the robot evaluates also that the robot's behavior using RNN is similar to the behavior generated by a potential fields approach for navigation. Our objective was to prove that GA is a good option as a method for finding behaviors for mobile robots’ navigation and also that these behaviors can be implemented using RNN.

This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.

This paper presents a two-dimensional curvature-continuous path planning algorithm based on Voronoi diagrams and Fermat's spiral segments. The map and the obstacles position are assumed to be known a-priori and static. Despite the disposition of the obstacles, the Voronoi diagram always presents at least one collision-free path, maximally distant from all the obstacles. If more than one flyable path is available, the shortest one is selected. The result is further refined to obtain a more practical path that is piecewise linear with discontinuous curvature and velocity. Fermat's spirals are used to smooth the path and provide curvature-continuity. A maximum threshold for the curvature is set so that the result of the algorithm respects kinematics and dynamics constraints of the vehicle. Moreover a minimum clearance from the obstacles can be chosen to respect additional safety constraints. The final result of the algorithm is a simple and intuitive path composed only by straight lines and spiral segments.

This study discusses the autonomous obstacle avoidance system by combined control of braking and steering as an active safety technology to prevent accidents, focusing on how to consider the limitation of steering angular velocity of the automatic steering system in order to improve its effectiveness. A modification method of the desired steering angle profile is proposed and the validity of the proposed method is verified by numerical simulations and real car experiments. The results indicate that the proposed method improves the effectiveness of the autonomous collision avoidance system.

Leveraging new technology in vehicle actuation and sensing, the authors present a control framework for obstacle avoidance and stability control using safe driving envelopes. One of these envelopes is defined by the vehicle handling limits and the other is based on spatial limitations imposed by the environment. A Model Predictive Control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while allowing the driver access to the full capabilities of the vehicle.

In this work, indoor guidance and control of an autonomous holonomic transport robot vehicle via wireless (ZigBee) communication modules is presented. The vehicle modified as a mini forklift is equipped with four mecanum wheels which are separately driven with geared DC- Motors. In this configuration it has the capability of moving omnidirectional and to work even in very narrow areas. For indoor guidance and control of the autonomous unmanned vehicle (AUV), a remote control system existing of ZigBee wireless RF Modules, a PC as main controller and Ardupilot Mega (APM) microcontroller kit is implemented. Stationary PC as main controller can guide the vehicle to follow a calculated path autonomously through bilateral data transmission between APM and PC. As control data the angular position (yaw angle) is detected through integrated IMU (Inertial Measuring Unit) of APM and as second control parameter linear position is measured by an encoder. Besides navigation a self developed algorithm is integrated to the system for possible obstacle avoidance and optimal path finding. A Sharp distance sensor is used to detect distance and obstacles in the environment. Through integrated IMU and GPS system of APM the vehicle can be used for both indoor and outdoor applications. The results of experimental testing showed the effectiveness of the proposed approach.

In this study, a ray-mimic underwater robot was designed and implemented. The robot is propelled by its pectoral fins. The robot is equipped with IR sensors and an image sensor for obstacle avoidance and object racking, respectively. Its specific gravity is adjusted by two water storage tanks and its center of gravity is controlled by two screw-rods. The image data is processed on board using a DSP chip installed in the robot and motion control is achieved by PIC16F877 micro-controllers. Obtained data are also sent to its user via wireless communications so that real time performance can be accomplished. Experimental results show that the robot achieves its designed functions successfully.

Modern active 3D-cameras open exciting opportunities for real-time distance measurements and obstacle avoidance in robotics. Here a challenging space technology problem related to rendezvous of a spacecraft with an non-cooperative target is supported by a Photonic Mixer Device (PMD) sensor. A key aspect is here the extension of the limited measurement range related to this sensor technology. Earlier approaches relied on phase jumps in the image to recover the true distance from the interferometric measurement bias. For space applications it is important to elaborate alternative approaches, as there is no contiguous distance span available. The range extension of the PMD sensor presented is a part of a model-based uncooperative object tracker. Thus performance characterisations for object tracking test results are provided, as well as perspectives for further application potential of the PMD sensor.

This paper considers a collision avoidance problem of the vehicle and the moving obstacle. The prohibited region is defined for the vehicle and the obstacle considering a specified size of them. The problem is formulated as a mixed integer programming problem. In the problem the obstacles and environments around the automobile can be represent as inequality conditions. Then the driver assistance algorithm for the collision avoidance using the feasibility of the optimization is proposed. Computer simulation shows that the proposed algorithm can provide appropriate control input for collision avoidance.

The design of robust vision based robot navigation behaviors remains a challenge in mobile robotics as it requires a coherent mapping between a complex visual perception and its associated robot motion. This contribution proposes a framework to learn this general relationship from a small set of representative demonstrations in which an expert manually navigates the robot through its environment. Behaviors are represented by a dynamic system that ties the perceptions to actions. The state of the behavioral dynamics is characterized by a small set of visual features extracted from an omnidirectional image of the local environment. Recording, learning and generalization takes place in the product space of visual features and robot controls. Training instances are recorded for three distinctive behaviors namely corridor following, obstacle avoidance and homing. Behavioral dynamics are represented as Gaussian mixture models, parameters of which are identified from the recorded demonstrations. The learned behaviors are able to accomplish the task across a diverse set of initial poses and situations. In order to realize global navigation, the behaviors are coordinated via hand designed arbitration or command fusion schemes. The experimental validation of the proposed approach confirms that the acquired visual navigation behaviors in cooperation accomplish robust navigation in indoor environments.

This paper presents a hybrid navigation and control method for intelligent wheelchair. A robust and semi-supervised vision tracking method using Microsoft Kinect and obstacle avoidance navigation approach are integrated into a two-level control system. Then, position tracking control considering nonholonomic constraints are adopted for the motion control of wheelchairs. An indoor environment is set up for evaluating the application of the proposed approaches. The experimental results show that the proposed method is effective and can be used as a hands-free controller for the intelligent wheelchair.

This work, presents an application for static obstacle avoidance and optimal path finding of an autonomous robot vehicle with navigation and control. Angular position and location of the vehicle is measured intermittent by means of an electronic compass sensor and a linear position encoder for the navigation purposes. Vehicle movements and orientation is controlled real time by a stationary computer. Besides navigation a self developed algorithm is integrated to the system for static obstacle avoidance and optimal path finding. An environmental map to detect the static obstacle positions are pre-defined and different maps depended on working area are evaluated by using a suitable computer program. To guide the vehicle to a given target, an optimal path calculation algorithm is applied. During high level control operations are executed by the computer, low level operations are made by a microcontroller to minimize time depended errors. The results and simulations of experimental testing showed the effectiveness of the proposed approach for navigation applications. © 2012 IFAC.

The wheeled mobile robots used in the flexible manufacturing systems act in an environment with static and dynamic obstacles. This paper proposes a new control method for the wheeled mobile robots movement in the presence of static and dynamic obstacles. The dynamic model used for steering and obstacle avoidance is the differential equations system. The environment is the commonly used laser range finder (LRF) system. The obstacle avoidance control is solved using the trajectory tracking control. The sliding mode control approach is used for the trajectory tracking problem. The effectiveness of the proposed local navigational system in an unknown environment with static and moving objects, corresponding to flexible manufacturing system, is proved through simulation results.

This paper proposes a robust on-line and adaptive elliptic trajectory for reactive obstacle avoidance. These trajectories permit a safe and smooth mobile robot navigation in cluttered environment. Indeed, they use limit-cycle principle already applied in the literature Adouane et al. (2011). The main contribution proposed here is to perform this navigation in a completely reactive way while using only uncertain range data. Each obstacle, in this obstacle avoidance strategy, is surrounded by an ellipse and its parameters are obtained online while using the sequence of uncertain range data. This method uses the fusion between heuristic approach and Extended Kalman Filter (EKF) techniques to improve the computed ellipse parameters. A large number of simulations and experiments show the efficiency of the proposed on-line navigation in cluttered environment.

The walking paths of humans in everyday life exhibit specific characteristics. Our investigation takes the well-established perspective that human locomotion approximately fulfills suitable optimality principles when walking from a starting position to a designated end position. Here, we address the special task of collision avoidance with a crossing interferer. While our model of the dynamics is quite simple, we focus on the task of determining the cost function out of a parametric family, that results in a best fit between the corresponding optimal control-based navigation and given recorded data of human navigation. The resulting bilevel optimization problem combines an optimal control problem on the lower level with a data fitting problem on the upper level. Our solution strategy uses the first-order optimality conditions of the (discretized) optimal control problem to transform the bilevel problem to a standard (one-level) optimization problem. Modeling aspects concerning the walking process and especially the interfering person are discussed. Since human walking motions with a crossing interferer do not seem to be globally optimal, an MPC-like approach distinguishing between obstacle avoidance and free motion is introduced and optimization results using recorded human data are presented.

With an increasing demand throughout industry for autonomous vehicles with more intelligent decision-making features, the issue of Unmanned Surface Vehicle (USV) navigation is addressed with respect to automatic obstacle avoidance. This Obstacle Detection and Avoidance (ODA) system design approach addresses the primary concern, the safety of a USV in operation. Due to the desire for improved autonomy and thus reduced human interaction, it is essential to incorporate vessel avoidance protocols into the ODA architecture for operation amongst maritime traffic, implementing the marine Rules of the Road. A heuristic Rule-based Repairing A* (R-RA*) algorithm is proposed, which is integrated within a decision-making framework. The integrated system has proven effective in simulations for path planning in real-time, in accordance with the International Regulations for Preventing Collisions at Sea.

There is widespread interest in the deployment of fleets of marine robots with the potential to roam the oceans freely and collect data at an unprecedented scale. This calls for the development of efficient algorithms for multiple vehicle motion planning that can take directly into account the capabilities of each vehicle and environmental conditions and lend themselves to seamless integration with control and navigation systems. The paper describes advances towards the development of a new breed of motion planning systems that address explicitly inter-vehicle collision avoidance, together with a number of criteria that may include simultaneous times of arrival at assigned target points, energy minimization, and acoustic communication constraints. The theoretical framework adopted is rooted in the so-called Projection Operator Approach that borrows from optimization and dynamical systems theory. Simulations with full dynamic vehicle models illustrate the potential of the methods developed.

This paper presents the RobonAUT contest organized by the Budapest University of Technology and Economics, Department of Automation and Applied Informatics. The contest aims to support the education of control theory, embedded system design and several other courses related to building autonomous mobile robots in a fun and instructive teamwork project. Students are required to build autonomous four-wheel robots capable to go along a path indicated by a continuous line on the ground, and to perform several tasks along the way. These tasks involve obstacle avoidance, parking operations, passing slopes, and acceleration segments. 3 students work together in a team during the semester to build their robot, for which they need skills related to control theory, both analog and digital circuit design, and microcontroller programming. The task has a very strong integrating nature by utilizing the material of several courses of electrical engineering. Beside the motivating fact that building an own robot is fun, we believe that students are given the opportunity to apply their knowledge to solve a real-world engineering task. And based on the popularity of the challenge, students seem to realize the need for this practice as well. This paper is aimed to describe the history, rules and experiences of the challenges in 2010 and 2011, together with the plans of the 2012 challenge.

In the last decade, the security of civilian transportation hubs has received an increasing focus. Civilian harbors are no exception, and to this aim Unmanned Surface Vehicles could be exploited for patrolling purposes and to investigate suspect situations, increasing the effectiveness of the harbor protection system while lowering the number of humans directly exposed to threats. The use of a team of USVs for the security of civilian harbors is the subject of an on-going research project between DIST and Selex-SI, a leading player for homeland security systems. This paper presents the latest results of the project, which are centered on the problem of intercepting a suspect vehicle. Both the off-line optimization of the positioning of the USV fleet and the on-line selection of the most suitable USV are tackled.

In this work, a novel approach for navigation, guidance and control of a duo cycle autonomous robot vehicle with dynamic obstacle avoidance and adaptive path finding algorithm is presented. Real time control and simulation of vehicle movements and orientation is made by a stationary computer. In addition, a self developed algorithm is integrated to the system for dynamic obstacle avoidance and adaptive path finding. Sub-optimal paths to the given target point are calculated simultaneously according to dynamic environment with a written algorithm program. Low level control operations are made by microcontroller to minimize time depended errors. Mobile computer is used to collect laser measurement data and provides the communication between vehicle and control station (stationary pc). Laser measurement sensor is used for simultaneous obstacle detection and environment mapping so that an adaptive like path finding can be realized. The simulations and experimental results showed the effectiveness of the proposed approach for navigation applications with adaptive path finding algorithm. © 2012 IFAC.

This paper describes the guidance of a group of autonomous cooperating vehicles using model predictive control. The developed control strategy allows to find a feasible near optimal control sequence with a short and constant computation delay in all situations. It makes use of the nonlinear model of the vehicle and takes other vehicle intentions into account. Numerical simulations are provided where a group of vehicles must reach several way-points while avoiding obstacles and collisions inside the group. These simulations allow to compare computation delay and efficiency of the proposed approach with traditional optimisation.

In traversing a planetary surface by a remote controlled rover, it is difficult to realize long distance traversability because of measurement error on terrain data and communication time delays. Though an operator can select any desired path as a sequence of waypoints by using a 3D terrain model measured by the on-board sensor, the model includes much error and the path might be collided with obstacles. Here a rover continuously updates its knowledge of the environment, and can recalculate the difference between the original terrain data used for initial path generation and the latest data acquired by itself. Therefore, we have proposed the waypoint compensation method CDC by using the latest measurement data (which is assumed to be more reliable than the initial data) that automatically corresponds to the difference. In this paper, CDC is evaluated by various simulation results, and discussed about newly developed tele-navigation test-bed system with its hardware and software aspects. Experimental results of the system using the proposed algorithm are indicated by including field test in Izu-oshima island, and finally its new evaluation is discussed.

International audience; This paper deals with a multi-mode control architecture for robot navigation and obstacle avoidance. It presents an adaptive and flexible algorithm of control which guarantees the stability and the smoothness of mobile robot navigation dealing with unexpected events. Moreover, the proposed Planning and Re-Planning (PRP) algorithm combine the two schools of thought, the one based on the path planning to avoid obstacles and reach the target, described as cognitive, and the second using the reactive algorithms. In fact the mix of these two approaches allows us to develop a very reliable algorithm. It provides us a scalable mobile robot navigation and obstacle avoidance, with less processing. It is accomplished by making an initial path planning, then to resolve the problem of unexpected static or dynamic obstacles while tracking the trajectory. A system of hierarchical action selection allows us to switch to a reactive avoidance, then to re-plan a new and safe trajectory to reach the target. A large number of simulations in different environments are performed to show the efficiency of the proposed PRP algorithm.

An efficient potential flow solver is applied to marine vessel path-planning for cluttered environments. The approach is computationally light. This is achieved by employing a simplified potential flow solution as well as generating the path online. The method relies on a simple guidance law that; (A)Regulates a target point to maintain a constant stream-function value, (B)Employ rudder control to steer a vessel towards this target. The utility of potential flow for path-planning is discussed. Simulation results are presented, showing the promise of the above approach.

An algorithm called Finite Receding-Horizon Incremental-Sampling Tree (RH-IST) will be presented. It contains elements from the well-known Rapidly Exploring Random Tree algorithm and receding-horizon framework to provide a computationally inexpensive planning algorithm for vehicles with limited sensing capability in uncharted environments. To resemble a finite planning horizon property in the algorithm, a deterministic configuration space sampling is conducted on the surface of a sphere around the root of the tree. To produce near optimal trajectories, samples are connected according to their cumulative costs, resulting in an optimization heuristic. In the tree construction phase, each branch of the tree consists of the trajectory of a fixed-wing unmanned aerial vehicle. The steering method necessary to construct the branches comprises of a closed-loop time-domain simulation on guidance level with a non-linear 3-D mass point representation of the plant, a path following controller and a reference path based on Bezier-splines. The feasible best branch of the RH-IST is chosen to provide the input for the execution of the plant based on the evaluation of a cost-functional. Simulations demonstrate the adaptive behavior of the RH-IST in a 3-D urban environment with static and unknown obstacles.

The well-known potential field method for obstacle avoidance in the scope of mobile robots is discussed in this paper. Particular attention is on the car-like mobile robots, which impose practical limitations on the application of potential field method due to its limited speed and curvature in motion. Along with the review of some recent studies on this topic, we point out the necessity of implementing a nonholonomic motion planner and propose some extensions to other potential-field-related methods to deal with the constraints of car-like robots. Two exemplary scenarios based on our extensions are simuated to prove their feasibility in application.

The Eurobot contest is an international robot building competition. This paper presents an autonomous robot mechanical and electric design built to Eurobot 2011 robotics contest. The robot collects the playing elements, builds towers and avoid collision with the opponent robot. The building process, the control and vision algorithms are also presented. We specify the used absolute (ultrasonic sonar) and relative (odometry) positioning methods, and we describe their advantages and disadvantages. The hardware and software architecture, navigation algorithms are also presented. This paper focuses on the educational aspects of the project. We demonstrate how our team collaborates, and how the tasks were assigned to them in order to comply their thesis.

Development of efficient and practical path-planning methods is a key issue for modern autonomous underwater vehicles (AUV). This paper describes a streamline function based obstacle avoidance path planning method for autonomous underwater vehicle. The specific requirement of the underwater world, AUV manoeuvrability, and constrained view of sonar were considered by the modification of streamline function and numerical techniques. This method is less affected by local minima than classical methods using the potential velocity. The streamline function methods for single and multiple obstacles in two dimensions were discussed with constrained searching space and the detailed parameters conditions. The simulation results confirm that this method is feasible and suitable for autonomous underwater vehicles.

The sense of telepresence is very important in teleoperation environments in which the operator is physically separated from the vehicle. It appears reasonable, and it has already been shown in the literature, that extending the visual feedback with force feedback is able to complement the visual information (when missing or limited) through the sense of touch and allows the operator to better perceive information from the remote environment and its constraints, hopefully preventing dangerous collisions. This paper focuses on a novel concept of haptic cueing for an airborne obstacle avoidance task; the novel cueing algorithm was designed in order to appear “natural” to the operator, and to improve the human-machine interface without directly acting on the actual aircraft commands. An experimental evaluation of two different Haptic aiding concepts for obstacle avoidance is presented. An existing and widely used approach, belonging to what we called the Direct Haptic Aid (DHA) class, and a novel one based on the Indirect Haptic Aid (IHA) class. The two haptic aids were compared with a baseline condition in which no haptic force was associated to the obstacles. Test results show that a net improvement in terms of performance (i.e. the number of collisions) is provided by employing the IHA haptic cue instead of both the DHA haptic cue and the visual cue only. Most participants of the experiment reported the strongest force feeling, the most necessary effort and also the most helpful sensation with DHA and IHA conditions with respect to the baseline condition. This paper shows that the IHA philosophy is a valid alternative to the other commonly used, and published in the scientific literature, approaches which fall in the DHA category.

Humans acquire manipulation skills by trial and error within a few trials, whereas programming a robot to perform the same task requires robotic expertise and effort. This paper presents a robot which learns a movement from demonstrations with the ability to generalize the movement to new goal poses and avoid the collision with obstacles in the workspace. The general movement is represented by dynamic movement primitives (DMP) augmented by potential fields in order to modulate the motion in the presence of obstacles. The approach is validated in experiments with a robotic arm in which dynamic obstacles partially blocking the movement are detected by a Photonic-Mixer-Devices (PMD) camera.

In this paper, we consider the general problem of guaranteeing safe interaction of humans and a group of mobile agents in an environment of interest. The way that we guarantee such a safe interaction is via proposing algorithms that prevent the agents from colliding with environmental obstacles with unknown sizes and locations, e.g. human operators. To do so, we first consider the problem of forcing a set of autonomous agents form a desired formation and follow a trajectory only known to one of them, namely the leader of the formation. Then we build upon the solution given to this problem and solve another problem of interest. This is to guarantee obstacle collision avoidance while the agents are following an unknown trajectory and keeping the desired shape of the formation. The stability of the control laws presented here is analytically established and some simulation results are presented to show the performance of the proposed control laws in different scenarios.

This paper presents a real-time implementation of a collision avoidance (CA) system that uses autonomous braking and model predictive control to assist drivers in avoiding collisions with other road users. To the authors knowledge, this is the first CA system that targets general vehicle collisions that has been implemented in a car. The system is based on a recently published decision-making algorithm which is described in [1]. To validate the CA system in various collision scenarios without endangering the driver of the vehicle, a novel test platform has been developed. The test platform consist of a soft crashable obstacle which is movable in speeds up to 70 km/h and safe to collide with in any angle in relative speeds up to 100 km/h. In the current implementation, estimates of the motion of the obstacle are obtained through a reference sensor fusion system that is based on a combination of in-vehicle sensors and a differential global positioning system. Results from both intersection and rear-end collision situations are presented. The results show that the proposed CA system can be implemented in a real-time environment and that the predictive brake control algorithm accurately accounts for delays and ramp-up times in the brake system of the vehicle.

This paper deals with leader-following formation control of a group of wheeled mobile robots (WMRs) when the leadership alternates among the robots of the group. In the problem presented here, firstly a scout robot is used to search for a path in a previously unknown environment with static obstacles. The path found by this scout is used as a reference for the leader of the multi-robot formation. As the group follows the trajectory preserving the formation, whenever any robot in the formation is closer than a limit threshold distance from an obstacle, this robot assumes the leadership. So the new leader must adequate its position to the reference trajectory, and, as a result of the formation control, the position of the whole group is also adjusted. This way, the whole group navigates avoiding collisions with obstacles. The mobile robots exchange their position information among themselves according to a prespecified communication directed graph (digraph). A different digraph is defined for each robot as a leader. Simulation results are presented for the formation control applied on this scenario.

Effective target tracking and obstacle avoidance strategies are essential to the success of unmanned aerial vehicle (UAV) missions. This paper describes a decentralized model-based predictive control to calculate the optimal UAV trajectory that will lead each UAV to the interception of a target at a desired time and with desired speed, heading and flight path angle, while avoiding dynamic ellipsoidal obstacles detected en route. Obstacle avoidance is based on the minimization of the UAV collision probability with all known obstacles on a future horizon, while ensuring that the collision probability with any given obstacle at each prediction step does not surpass a preset threshold. Cooperation between UAVs is possible by exchanging information about the obstacles they detect. To respect the arrival specifications, a virtual target is created and tracked. The virtual target moves at the desired speed and along a straight line that is correctly oriented to intercept the real target position at the desired arrival time. Arrival specifications are respected if tracking of the virtual target by the UAV is successful. Simulations are presented to demonstrate the effectiveness of the proposed approach. They also illustrate that cooperation can improve the UAV performance.

This paper investigates the application of differential geometry to UAV collision avoidance based on differential geometry. The Differential Geometry approach has been useful in determining a guidance algorithm to produce a constant curvature evasion manoeuvre to avoid collision with an Aircraft flying in a straight line. Simulation results presented in the paper demonstrated the effectiveness of the proposed UAV collision avoidance algorithms.

This paper presents a control system architecture for robot manipulator suitable for on line path planning in a cluttered environment. The proposed technique fits for both redundant and non redundant manipulators. We use potential fields in order to control the joint velocities to make the manipulator escape from obstacles. We also propose a new weighted pseudoinverse matrix that improves the manipulator capability to find feasible paths moving around obstacles and passing through narrow corridors without calculating the manipulator mass matrix. The proposed solution is tested on two manipulators: a 7 degree-of-freedom (DOF) planar manipulator and a 7 degree-of-freedom spatial manipulator both moving in a cluttered environment.

This paper proposes a hierarchical MPC strategy for autonomous navigation of a formation of unmanned aerial vehicles (UAVs) of quadcopter type under obstacle and collision avoidance constraints. Each vehicle is stabilized by a lower-level local linear MPC controller around a desired position, that is generated, at a slower sampling rate, by a hybrid MPC controller per vehicle. Such an upper control layer is based on a hybrid dynamical model of the UAV in closed-loop with its linear MPC controller and of its surrounding environment (i.e., the other UAVs and obstacles). The resulting decentralized scheme controls the formation based on a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations and comparisons with other path planning strategies, showing the ability of linear MPC to handle the strong couplings among the dynamical variables of each quadcopter under motor voltage and angle/position constraints, and the flexibility of the decentralized hybrid MPC scheme in planning the desired paths on-line.

International audience; We consider minimal time problems governed by nonlinear systems under general time dependant state constraints and in the two-player games setting. In general, it is known that the characterization of the minimal time function, as well as the study of its regularity properties, is a difficult task in particular when no controlability assumption is made. In addition to these difficulties, we are interested here to the case when the target, the state constraints and the dynamics are allowed to be time-dependent. We introduce a particular "reachability" control problem, which has a supremum cost function but is free of state constraints. This auxiliary control problem allows to characterize easily the backward reachable sets, and then, the minimal time function, without assuming any controllability assumption. These techniques are linked to the well known level-set approachs. Partial results of the study have been published recently by the authors in SICON. Here, we generalize the method to more complex problems of moving target and obstacle problems. Our results can be used to deal with motion planning problems with obstacle avoidance.