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In a given manufacturing setting where workers or robots are coexisting in a confined area and their movements are not coordinated due to loss in communication or because they are freely ranging relative to each other, the development of an onboard safeguard system for a robot becomes a necessity to reduce accidents while the production efficiency is uncompromised. This article develops a two-dimensional dynamics model that predicts the relative position between a robot's end-of-arm tooling and an approaching object or threat. The safety strategy applied to the robot is derived from the calculation of three parameters: the time of collision predicted from the linear motion between the approaching object and the robot's end-of-arm tooling, the relative absolute distance, and the overlapping area ratio. These parameters combined are updated in a cost function that is sufficiently alarming the collision severity of an approaching object in real time. This model enables deployment a safe and a productive collaborative interaction in the manufacturing environment where workers and robots are seemingly moving in close proximity within an open workspace with less safeguard barriers. [ABSTRACT FROM AUTHOR]

In this paper we show how the quantum-based particle swarm optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum Robot Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on the QRDPSO formulation and parameters control which show how the swarm overcomes communication constraints to avoid obstacles and achieve optimal solution. The results show the QRDPSO is an upgrade over RDPSO in terms of convergence speed, trajectory control, obstacle avoidance and connectivity performance of the swarm. [ABSTRACT FROM AUTHOR]

Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system. [ABSTRACT FROM AUTHOR]

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the local path planning for unmanned surface vessel (USV) with towed cable, a virtual repulsive field based modified artificial potential field approach is proposed. Firstly, the model of USV and its towed cable is established, then the problems of "USV+ towed cable" joint collision avoidance and effective tension exceeding threshold are analyzed. Secondly, the arterial potential field approach is modified by introducing a virtual repulsive field, and the safety margin of the towed cable is increased based on the effective obstacle avoidance for USV hull. Finally, the cable safety is guaranteed and the effective obstacle avoidance of the USV with towed cable is realized. The simulation and sea test result show that the deigned approach could comprehensively guarantee the motion safety of USV and its towed cable. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment. [ABSTRACT FROM AUTHOR]

Self-collisions of a dual-arm robot system can cause severe damage to the robot. To deal with this problem, this paper presents a real-time algorithm for preventing self-collisions in dual-arm systems. Our first contribution in this work is a novel collision model built using discrete spherical bounding volumes with different radii. In addition, we propose a sensitivity index to measure the distance between spheres with different radii in real time. Next, according to the minimal sensitivity index between different spheres, the repulsive velocity is produced at the centers of the spheres (control points), which the robot uses to generate new motion based on the robot kinematic model. The proposed algorithm offers the additional benefits of a decrease in the number of bounding spheres, and a simple collision model that can effectively decrease the computational cost of the process. To demonstrate the validity of the algorithm, we performed simulations and experiments by an upper-body humanoid robot. Although the repulsive velocity acted on the control points, the results indicate that the algorithm can effectively achieve self-collision avoidance by using a simple collision model. [ABSTRACT FROM AUTHOR]

The cooperative motion of multiple mobile robots has attracted wide attention due to its advantages in military, marine and aerospace fields, and formation control has become a significant technology in the realization of these tasks. However, most of the existing formation control designs of mobile robots do not consider the practical obstacles in the environment, and the maintenance of both formation and trajectory tracking while confronting the obstacles is still a challenging issue. Therefore, in this paper, a virtual-structure-based formation control approach is designed with obstacle avoidance for a system with multiple mobile robots. The basic trajectory is generated for each robot in the group and parameterized to keep the group in formation. A trajectory generator is then established regarding the obstacles, where a potential function is designed to adjust the basic trajectory and replan the reference trajectory to achieve obstacle avoidance. Then, a novel design for the path parameter is proposed to improve the performance of the robot group when encountering obstacles. Finally, a tracking controller is designed to achieve good tracking performance for robots, and the guaranteed performance is achieved via the Lyapunov theorem. A comparative simulation with three sets is carried out, where an objective function F o b j is designed to evaluate the tracking performance in the presence of obstacles. Besides this, a real experiment is implemented to further verify the effectiveness. The simulation and experimental results verify the good formation and tracking performance of the proposed design for a system with multiple mobile robots with obstacle avoidance. [ABSTRACT FROM AUTHOR]

In this paper, a CNN-based learning scheme is proposed to enable a quadrotor unmanned aerial vehicle (UAV) to avoid obstacles automatically in unknown and unstructured environments. In order to reduce the decision delay and to improve the robustness for the UAV, a two-stage end-to-end obstacle avoidance architecture is designed, where a forward-facing monocular camera is used only. In the first stage, a convolutional neural network (CNN)-based model is adopted as the prediction mechanism. Utilizing three effective operations, namely depthwise convolution, group convolution and channel split, the model predicts the steering angle and the collision probability simultaneously. In the second stage, the control mechanism maps the steering angle to an instruction that changes the yaw angle of the UAV. Consequently, when the UAV encounters an obstacle, it can avoid collision by steering automatically. Meanwhile, the collision probability is mapped as a forward speed to maintain the flight or stop going forward. The presented automatic obstacle avoidance scheme of quadrotor UAV is verified by several indoor/outdoor tests, where the feasibility and efficacy have been demonstrated clearly. The novelties of the method lie in its low sensor requirement, light-weight network structure, strong learning ability and environmental adaptability. [ABSTRACT FROM AUTHOR]

This paper deals with a novel procedure to generate optimum flight paths for multiple unmanned aircraft in the presence of obstacles and/or no-fly zones. A real-time collision avoidance algorithm solving the optimization problem as a minimum cost piecewise linear path search within the so-called Essential Visibility Graph (EVG) is first developed. Then, a re-planning procedure updating the EVG over a selected prediction time interval is proposed, accounting for the presence of multiple flying vehicles or movable obstacles. The use of Dubins curves allows obtaining smooth paths, compliant with flight mechanics constraints. In view of possible future applications in hybrid scenarios where both manned and unmanned aircraft share the airspace, visual flight rules compliant with International Civil Aviation Organization (ICAO) Annex II Right of Way were implemented. An extensive campaign of numerical simulations was carried out to test the effectiveness of the proposed technique by setting different operational scenarios of increasing complexity. Results show that the algorithm is always able to identify trajectories compliant with ICAO rules for avoiding collisions and assuring a minimum safety distance as well. Furthermore, the low computational burden suggests that the proposed procedure can be considered a promising approach for real-time applications. [ABSTRACT FROM AUTHOR]

In this paper, we study the output-feedback formation tacking control problem for a group of unmanned surface vehicles (USVs) with modeling uncertainties under communication constraints. We consider a one-to-one communication topology, in which the leading vehicle is assigned a task to track a desired trajectory and each vehicle except for the last follower (tail agent) communicates only with one leader and with one follower. We assume that the information exchange among the vehicles is limited by some given communication radius. Under the limited communication range, connectivity maintenance and collision avoidance between the leader and follower are considered in the formation tracking control design. To compensate for the modeling uncertainties, we employ neural network (NN) approximators to estimate uncertain dynamics. Based on the dynamic surface control technique, backstepping procedure, NN-based observers, tan-type barrier Lyapunov functions, and control Lyapunov synthesis, a decentralized adaptive output-feedback formation tracking controller is presented to achieve the boundedness of the signals in the closed-loop system, while guaranteeing connectivity maintenance and collision avoidance between the leader and follower during whole operation. Simulation results demonstrate the effectiveness of the proposed formation controller. [ABSTRACT FROM AUTHOR]

In this paper, group formation control with collision avoidance is investigated for heterogeneous multiquadrotor vehicles. Specifically, the distance-based formation and tracking control problem are addressed in the framework of leader-follower architecture. In this scheme, the leader is assigned the task of intercepting a target whose velocity is unknown, while the follower quadrotors are arranged to set up a predefined rigid formation pattern, ensuring simultaneously interagent collision avoidance and relative localization. The adopted strategy for the control design consists in decoupling the quadrotor dynamics in a cascaded structure to handle its underactuated property. Furthermore, by imposing constraints on the orientation angles, the follower will never be overturned. Rigorous stability analysis is presented to prove the stability of the entire closed-loop system. Numerical simulation results are presented to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

There are growing interests in the use of robots in collaborative environments with humans or other intelligent machines. Sensing the environment for which the robot is operating can be done in many ways, generally guided by skin-like sensors. Some of the skins are inspired by natural sensing in humans or other species. As humans, we use many of our senses, such as version, hearing, smell, and touch, to move around by avoiding colliding with other humans or objects. Different from humans, many other mammals also use whiskers as an additional sensor to help navigate around. In this paper, we demonstrate a touchless capacitive imaging-based sensor in the situation where the obstacles are in close vicinity to the robot. The proposed imaging system can sense the changes in areas near to the skin-like sensors by measuring the capacitances between the array of electrodes. A 4D sensing approach has been developed with the spatiotemporal Total Variation algorithm. The 4D operational mode gives sensors the time awareness that allows for dynamical responses and hence the better control of the robots. Several experiments are conducted to show the skin-like behaviour of this sensor by simulating various scenarios. The sensor shows the excellent ability to detect an object in its vicinity, where the depth is close to half of the planar sensor array size. [ABSTRACT FROM AUTHOR]

Background: Fajen and Warren's steering dynamics model can reproduce human paths around an extended barrier by adding 'waypoints' at each end - if one waypoint is selected to minimize the global path curvature (Gérin-Lajoie and Warren, 2008). We propose that waypoint selection behaves like a choice between two competing goals, in which the smaller distance (d) and deviation angle (β) is preferred (Ulrich and Borenstein, 1998). Here we manipulate these two variables to test the determinants of route selection.Research Question: How does route selection in barrier avoidance depend on the local distance (d) and deviation angle (β) of each end, and on global path length (P) and curvature (C)?Methods: Participants (Exp1 N = 19; Exp2 N = 15) walked around a barrier to a visible goal in a virtual environment. Barrier orientation and lateral position were manipulated to vary the difference in distance (Δd) and in deviation angle (Δβ) between the left and right ends of the barrier. Left/Right route data were analyzed using a mixed-effects logistic regression model, with Δβ, Δd, and observed ΔP and ΔC as predictors.Results: The main effects of Δβ and Δd significantly predicted Rightward responses (p < .001), more strongly than ΔP and ΔC (ΔBIC = 29.5). When Δβ and Δd agreed, responses were toward the smaller distance and deviation (88% overall); when they conflicted, responses were in between (65% toward smaller β). The 75% choice threshold for Δβ was ±1.65˚, and for Δd was 0.75 m, from the 50% chance level.Significance: During barrier avoidance, participants select a route that minimizes the local distance (d) and deviation angle (β) of the waypoint, rather than the global path length (P) or path curvature (C). These findings support the hypothesis that route selection is governed by competing waypoints, instead of comparing planned paths to the final goal. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments. [ABSTRACT FROM AUTHOR]

Collision avoidance ability is very important for autonomous agricultural vehicles, but the influence of different obstacles in agricultural environment is rarely taken into account. In this paper, a velocity control strategy for collision avoidance was proposed to adjust the velocity of autonomous agricultural vehicles according to the movement state and dangerous degree of the obstacles and the distance between the obstacles and the vehicles, thus to improve intelligence and safety of the vehicles. The control strategy involved two steps: collision prediction in dynamic environments with an improved obstacle space–time grid map, and velocity generator for collision avoidance with a cloud model. Simulations were conducted on the obstacle collision prediction and the designed cloud generator for velocity control respectively. Simulation results show that the proposed strategy can effectively predict collision with anti-disturbance ability for threat-free obstacles and rapid and accurate velocity output. And it realizes the real-time operation in dynamic environments with an average time of 0.2 s to predict collision. Additionally, field experiments including five trial schemes were performed to test the proposed velocity control strategy on an agricultural robot, where a haystack, a tractor and walking persons were regarded as static or dynamic obstacles. The results of the field experiments show that the proposed velocity control strategy has strong feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

In order to study the adaptability of a multi-redundancy and multi-degree-of-freedom snake-like robot to underwater motion, a two-dimensional (2-D) obstacle avoidance control algorithm for a snake-like robot based on immersed boundary-lattice Boltzmann method (IB-LBM) and improved artificial potential field (APF) is proposed in this paper. Firstly, the non-linear flow field model is established under the framework of LBM, and the IB method is introduced to establish a fluid solid coupling of a 2-D soft snake-like robot. Then, the obstacle avoidance of a snake-like robot in a flow field is realized by optimizing the curvature equation of the serpentine curve and eliminating the local minimum in APF method. Finally, the effects by exerted different control parameters on a snake-like robot's obstacle avoidance capability are analyzed via MATLAB simulation experiment, by which we can find the optimal parameter of the obstacle avoidance and testify the validity of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor trajectory tracking and obstacle avoidance. Comparative simulations and Monte Carlo tests are carried out to demonstrate the effectiveness and robustness of the CGWO-based backstepping control scheme and virtual target guidance approach. [ABSTRACT FROM AUTHOR]

This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method. [ABSTRACT FROM AUTHOR]

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots' states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent's steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy's robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller's robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca. [ABSTRACT FROM AUTHOR]

Autonomous underwater gliders are robotic underwater vehicles that do not require operator input. They are frequently deployed in large-scale, long-term ocean-sampling missions that take advantage of their buoyancy-driven engines and very low power consumption. A limitation of the buoyancy-driven engine design is that the glider must travel at slow speeds and is unable to confront strong upstream currents. Optimal path-planning to minimize the upstream-current effect and distance traversed is helpful to guide the glider navigating in the ocean. In a glider path-planning problem, reachability signifies that a minimal upstream-current effect along a path is achieved, while efficiency refers to optimizing path distance, to make it as short as possible. Reachability and efficiency are sometimes inconsistent goals in glider path-planning, and obtaining an optimal solution between both aspects of path-planning constitutes a multi-objective optimization problem. In order to discover an optimal path for glider safety, a parallel genetic approach to the glider path-planning is developed here. We present a novel scheme of upstream-current avoidance to solve the critical path-planning problem in deployment of multiple gliders. The benefits of the proposed approach are used to produce characteristic curves that express how improved cruising capability benefits path diversity. The reachability of an important scheme of upstream-current avoidance is validated by a maximum likelihood function. Moreover, a new crossover operator is proposed to encourage individuals of offspring in solution diversity. Numeric results demonstrate that the proposed path-planning approach determines an optimal path that reduces the upstream-current effect, and also shortens the cruising distance in multi-glider path-planning solutions. [ABSTRACT FROM AUTHOR]

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As avionics technologies have advanced, it is possible to perform many aerial applications which demand cooperative work with multiple Unmanned Aerial Vehicles (UAVs). Since one of the basic applications is reconnaissance, we focus on efficient cooperative reconnaissance. While random mobility models are useful for multi-UAVs reconnaissance, they suffer from overlapped reconnaissance problem that two or more UAVs reconnoiter a region at the same time. The overlapped reconnaissance also leads to imbalanced reconnaissance in which an area scanned by one UAV may be re-visited soon by the other UAV. Thus, we provide overlap avoidance schemes for the existing reconnaissance mobility models and enhance their performance. Throughout the simulations, we evaluate the effect of applying overlap avoidance in the existing models. The simulation results show that overlapped area is reduced by up to 20 times and 90%-coverage reaching time is improved by up to 19%. [ABSTRACT FROM AUTHOR]

In this work, we present a complete hybrid navigation system for a two-wheel differential drive mobile robot that includes static-environment- global-path planning and dynamic environment obstacle-avoidance tasks. By the given map, we propose a multi-agent A-heuristic algorithm for finding the optimal obstacle-free path. The result is less time-consuming and involves fewer changes in path length when dealing with multiple agents than the ordinary A-heuristic algorithm. The obtained path was smoothed based on curvature-continuous piecewise cubic Bézier curve (C2 PCBC) before being used as a trajectory by the robot. In the second task of the robot, we supposed any unforeseen obstacles were recognized and their moving frames were estimated by the sensors when the robot tracked on the trajectory. In order to adapt to the dynamic environment with the presence of constant velocity obstacles, a weighted-sum model (WSM) was employed. The 2D LiDAR data, the robot's frame and the detected moving obstacle's frame were collected and fed to the WSM during the movement of the robot. Through this information, the WSM chose a temporary target and a C2 PCBC-based subtrajectory was generated that led the robot to avoid the presented obstacle. Experimentally, the proposed model responded well in existing feasible solution cases with fine-tuned model parameters. We further provide the re-path algorithm that helped the robot track on the initial trajectory. The experimental results show the real-time performance of the system applied in our robot. [ABSTRACT FROM AUTHOR]

The major principle of the robotic vehicle is to examine the user voice command and perform the given user task and without the human presence inparticular area can controlthe robot via user voice input. To control the robot through user voice inputit requires android app to communicate via Bluetooth HC-05 module. Subsequently, the robotic vehiclehas skill-ability to senses the object and tells the user to keep away from the object with the aid of selecting another way with the help of Ultrasonic sensor module. Therefore, this paper offers mainly for physically disabled peoples. Thereby in future, they drive their own vehicle with a more safety and security from unexpected attempt at sudden hit-and-run cases due to automatic braking or slow down feature. For the hardware, customized Arduino will give the control over the motors that use to run the robotic vehicle. Ultrasonic sensors interact with the Arduino help in automatic braking of a vehicle on sudden obstacle detection. [ABSTRACT FROM AUTHOR]

Collision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them. [ABSTRACT FROM AUTHOR]

The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios. [ABSTRACT FROM AUTHOR]

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at theshortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planningof lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of antcolony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces theinducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that thealgorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacleavoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric methodbased on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. Theexperimental results show that the improved adaptive potential field ant colony algorithm has higher global search abilityand faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will becollision and take obstacle avoidance measures. [ABSTRACT FROM AUTHOR]

To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions. [ABSTRACT FROM AUTHOR]

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle. [ABSTRACT FROM AUTHOR]

Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape nor that the vehicle follows a desired speed trajectory. Instead, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component that cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations and through full-scale experiments aboard the R/V Gunnerus. [ABSTRACT FROM AUTHOR]

Mobile robotic systems must have the ability to guarantee safety for operating in close proximity with other moving objects. This paper aims to develop collision avoidance method based on velocity control with respect to several modal of moving objects in the vicinity of wheeled mobile robot. We propose a method that is called Hybrid Velocity Obstacles to avoid multi modal moving objects. This is different from Hybrid Reciprocal Velocity Obstacles approaches in the assumption that objects in the robot's surrounding move with arbitrary speed and directions. The main advantage of the method is in the ability to avoid unknown nonlinear trajectories of moving objects. Collision avoidance is achieved by considering static objects, other mobile robots, and moving objects with nonlinear trajectories. These different characteristics of robot's surrounding objects are calculated to produce avoidance velocity. Our approach is implemented in simulations for a two-wheeled differential-steering mobile robot in the environment contains moving objects. The results show that our approach is capable to avoid collision with multi modal moving objects by maintaining safety with average value 0.17 of Proximity Index. It is envisaged that the proposed method can be very useful for developing transport robot that operate in human environment. [ABSTRACT FROM AUTHOR]

With the wide application of mobile robots in unstructured environments, an obstacle avoidance system with good performance has become an important part of mobile robot systems. We propose an obstacle avoidance method for a mobile robot based on multi-sensor information fusion technology and a fuzzy neural network control algorithm. In view of complex working environments, a differential kinematics estimation model of a mobile robot is studied. A multi-sensor information fusion method based on the extended Kalman filter and a mobile robot obstacle avoidance algorithm based on fuzzy neural network control are then proposed. Finally, simulations and experiments are conducted, which demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: In this article, we investigate the formation control problem of multiquadrotor aircraft system subject to connectivity preservation and collision avoidance. First, based on the backstepping design method, a novel formation control algorithm is developed to enable multiquadrotor aircraft to move along the desired trajectory while becoming the predefined formation pattern. Meanwhile, the connectivity of the communication network is maintained and the collision among multiquadrotor is avoided. Second, for each quadrotor's desired attitude obtained by the formation controller, an improved finite‐time attitude tracking law is designed to guarantee that the desired attitude can be tracked by the real attitude in a fast finite time. Finally, a numerical example is given to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, an infinite-horizon optimal regulation problem is considered for a control-affine nonlinear autonomous agent subject to input constraints in the presence of dynamic avoidance regions. A local model-based approximate dynamic programming method is implemented to approximate the value function in a local neighborhood of the agent. By performing local approximations, prior knowledge of the locations of avoidance regions is not required. To alleviate the a priori knowledge of the number of avoidance regions in the operating domain, an extension is provided that modifies the value function approximation. The developed feedback-based motion planning strategy guarantees uniformly ultimately bounded convergence of the approximated control policy to the optimal policy while also ensuring the agent remains outside avoidance regions. Simulations are included to demonstrate the preliminary development for a kinematic unicycle and generic nonlinear system. Results from three experiments are also presented to illustrate the performance of the developed method, where a quadcopter achieves approximate optimal regulation while avoiding three mobile obstacles. To demonstrate the developed method, known avoidance regions are used in the first experiment, unknown avoidance regions are used in the second experiment, and an unknown time-varying obstacle directed by a remote pilot is included in the third experiment. [ABSTRACT FROM AUTHOR]

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We present results from sea trials for an autonomous surface vehicle (ASV) equipped with a collision avoidance system based on model predictive control (MPC). The sea trials were performed in the North Sea as part of an ASV Challenge posed by Deltares through a Dutch initiative involving different authorities, including the Ministry of Infrastructure and Water Management, the Netherlands Coastguard, and the Royal Netherlands Navy. To allow an ASV to operate in a maritime environment governed by the International Regulations for Preventing Collisions at Sea (COLREGs), the ASV must be capable of complying with COLREGs. Therefore, the sea trials focused on verifying COLREGs‐compliant behavior of the ASV in different challenging scenarios using automatic identification system (AIS) data from other vessels. The scenarios cover situations where some obstacle vessels obey COLREGs and emergency situations where some obstacles make decisions that increase the risk of collision. The MPC‐based collision avoidance method evaluates a combined predicted collision and COLREGs‐compliance risk associated with each obstacle and chooses the 'best' way out of dangerous situations. The results from the verification exercise in the North Sea show that the MPC approach is capable of finding safe solutions in challenging situations, and in most cases demonstrates behaviors that are close to the expectations of an experienced mariner. According to Deltares' report, the sea trials have shown in practice that the technical maturity of autonomous vessels is already more than expected. [ABSTRACT FROM AUTHOR]

This paper presents a formation control method to solve the moving target tracking problem for a swarm of unmanned aerial vehicles (UAVs). The formation is achieved by the artificial potential field with both attractive and repulsive forces, and each UAV in the swarm will be driven into a leader-centered spherical surface. The leader is controlled by the attractive force by the moving target, while the Lyapunov vectors drive the leader UAV to a fly-around circle of the target. Furthermore, the rotational vector-based potential field is applied to achieve the obstacle avoidance of UAVs with smooth trajectories and avoid the local optima problem. The efficiency of the developed control scheme is verified by numerical simulations in four scenarios. [ABSTRACT FROM AUTHOR]

Advances in robotics promote interest in applications in the poultry industry to reduce human labor; however, skeptics express concerns regarding potential animal stress stemming from poultry-robot interactions. The objective of this study was to evaluate the avoidance distance (AD), a parameter reflecting stress in an animal due to an approaching object, of broilers and laying hens to a human assessor and a robotic ground vehicle. ADs were determined for birds at different ages (2, 4, 6, and 8 week old broilers; 28, 47, and 66 week old brown hens; and 27, 46, and 70 week old white hens) in a commercial broiler house and two commercial cage-free hen houses. The poultry-robot ADs were determined at three robot operating speeds (0.2, 0.4, and 0.6 m s-1) and two operation frequencies (bi-weekly and daily, for broilers only). The results show that the overall poultry-human and poultry-robot ADs were, respectively, 48 to 82 cm and 83 to 110 cm for broilers, 54 to 87 cm and 114 to 131 cm for brown hens, and 71 to 76 cm and 92 to 99 cm for white hens when the birds experienced the robot on test days only. Broiler-robot ADs significantly decreased to 30 to 63 cm when the birds were exposed to the robot daily, reflecting less stress induced by the robot than by the human. Poultry ADs to the human and robot tended to decrease for older birds. Slower robot operating speeds led to shorter ADs for broilers at 6 and 8 weeks of age and for white hens at all ages but did not affect ADs for broilers at 2 and 4 weeks of age and brown hens at all ages. Slower robot operating speeds also reduced the bird fleeing speed (FS). It is concluded that the robot did not induce more stress than the human in the broilers. The baseline poultry-robot ADs obtained in this study provide valuable information for understanding poultry interactions with robots and may help optimize robot operations for application in the poultry industry. [ABSTRACT FROM AUTHOR]

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a software-based approach for collision avoidance that can be applied in human–robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance. [ABSTRACT FROM AUTHOR]

A multi-strategies obstacle avoidance method based on follow-up rotating vector field is proposed for UAV flight planning. The inter-UAVs repulsion with distance factor and the target directional gravity are used as the inter-UAVs control strategy. In the process of obstacle avoidance, the artificial potential field method is used outside the obstacle to guide the UAVs to sail to the target point. In the obstacle avoidance scope, the follow-up rotating vector field method is proposed to avoid convex polyhedral obstacles. Finally, smoothing strategy is conbined to the follow-up rotating vector field to obtain safety and smoothing path. The simulation results show that the proposed algorithm can solve the problem of "dead zone" and "jitter" in the track by combining various strategies, and realize the obstacle avoidance behavior while avoiding collision between machines. [ABSTRACT FROM AUTHOR]

There has been a renewed interest in recent times in airship technology owing to its potential usage for applications ranging from defense, scientific exploration, advertising to even remote monitoring. For airships to expand operational profile, further enhancement of configurational features and control development for full autonomy are key technologies gaining attention. In this paper, beginning with the mathematical modeling of a thrust-vectored airship, the integrated motion planning and controller development for vehicle autonomy, taking into account various uncertainties, are dealt with. A rapidly exploring random tree-based obstacle avoidance path planning exercise is carried out to chart out a trajectory in the presence of obstacles. Then, a neural network-based sliding mode controller is subsequently designed that learns the unknown equivalent control in sliding mode control framework to track the reference trajectory. Simulation results presented at the end demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]