Showing papers on "Collision avoidance published in 2022"

Reinforcement Learned Distributed Multi-Robot Navigation With Reciprocal Velocity Obstacle Shaped Rewards

Abstract: The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information. The novelty of this work is threefold: (1) using a set of sequential VO and RVO vectors to represent the interactive environmental states of static and dynamic obstacles, respectively; (2) developing a bidirectional recurrent module based neural network, which maps the states of a varying number of surrounding obstacles to the actions directly; (3) developing a RVO area and expected collision time based reward function to encourage reciprocal collision avoidance behaviors and trade off between collision risk and travel time. The proposed policy is trained through simulated scenarios and updated by the actor-critic based DRL algorithm. We validate the policy in complex environments with various numbers of differential drive robots and obstacles. The experiment results demonstrate that our approach outperforms the state-of-art methods and other learning based approaches in terms of the success rate, travel time, and average speed.

Multi-UAV Collaborative Transportation of Payloads With Obstacle Avoidance

Abstract: Control barrier functions have been widely studied and applied to safety-critical systems, including multi-agent obstacle avoidance problems. In this work, we apply control barrier functions to a collaborative transportation problem involving two unmanned aerial vehicles (UAVs) moving a payload around obstacles as they deliver it to a target location. We develop a target-tracking controller for the UAVs, which is constrained to meet the requirements of payload dynamics and obstacle avoidance. We also present simulation results to demonstrate the benefits of the proposed problem formulation for a multi-obstacle environment.

Autonomous Trajectory Generation Comparison for De-Orbiting with Multiple Collision Avoidance

Abstract: Over the past four decades, space debris has been identified as a growing hazard for near-Earth space systems. With limited access to space debris tracking databases and only recent policy advancements made to secure a sustainable space environment and mission architecture, this manuscript aims to establish an autonomous trajectory maneuver to de-orbit spacecrafts back to Earth using collision avoidance techniques for the purpose of decommissioning or re-purposing spacecrafts. To mitigate the risk of colliding with another object, the spacecraft attitude slew maneuver requires high levels of precision. Thus, the manuscript compares two autonomous trajectory generations, sinusoidal and Pontragin’s method. In order to determine the Euler angles (roll, pitch, and yaw) necessary for the spacecraft to safely maneuver around space debris, the manuscript incorporates way-point guidance as a collision avoidance approach. When the simulation compiled with both sinusoidal and Pontryagin trajectories, there were differences within the Euler angle spacecraft tracking that could be attributed to the increased fuel efficiency by over five orders of magnitude and lower computation time by over 15 min for that of Pontryagin’s trajectory compared with that of the sinusoidal trajectory. Overall, Pontryagin’s method produced an autonomous trajectory that is more optimal by conserving 37.9% more fuel and saving 40.5% more time than the sinusoidal autonomous trajectory.

Distributed Formation Navigation of Constrained Second-Order Multiagent Systems With Collision Avoidance and Connectivity Maintenance

Abstract: In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints. Both collision avoidance and connectivity maintenance of the network are considered in the controller design. A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints. First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs. A nonsmooth analysis is employed to prove the global convergence of the controller. Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived. The barrier function-based collision-avoidance conditions are also developed. All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance. Simulation results demonstrate the effectiveness of the proposed control strategy.

Adaptive Leader–Follower Formation Control of Underactuated Surface Vehicles With Guaranteed Performance

Abstract: This article studies the formation tracking control problem for a group of underactuated surface vehicles with guaranteed transient properties, including connectivity maintenance, collision avoidance, and tracking performance specifications. The formation is within the leader–follower control framework, in which every follower is controlled to track its leader and maintain a desired relative distance and bearing angle with respect to its leader such that the prescribed formation geometry is achieved based on local sensing capability. The onboard sensor systems are of limited range and angle of view, thus defining a cone of detectable region for every follower. Each follower can detect its leader, if and only if the relative distance and bearing angle keep always inside the predefined detectable region such that the connectivity between the follower and its leader is maintained over time. In addition to the consideration of connectivity maintenance, no collision between the follower and its leader is also considered. A transverse function control approach is introduced to overcome the difficulties caused by the off-diagonal system matrix and underactuation. The barrier Lyapunov function and adaptive backstepping procedure are incorporated into the formation control design to achieve the boundedness of the closed-loop systems with guaranteed transient performance. Collision avoidance and connectivity maintenance between every follower and its leader are also proven mathematically. Simulation studies are performed to show the effectiveness of the proposed control design technique.

Intelligent UAV Swarm Cooperation for Multiple Targets Tracking

Abstract: With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep $Q$ -network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

Reinforcement Learning-Based Collision Avoidance and Optimal Trajectory Planning in UAV Communication Networks

Abstract: In this paper, we propose a reinforcement learning approach of collision avoidance and investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks. Specifically, each UAV takes charge of delivering objects in the forward path and collecting data from heterogeneous ground IoT devices in the backward path. We adopt reinforcement learning for assisting UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance. In addition, for each UAV, we use optimization theory to find out a shortest backward path that assures data collection from all associated IoT devices. To obtain an optimal visiting order for IoT devices, we formulate and solve a no-return traveling salesman problem. Given a visiting order, we formulate and solve a sequence of convex optimization problems to obtain line segments of an optimal backward path for heterogeneous ground IoT devices. We use analytical results and simulation results to justify the usage of the proposed approach. Simulation results show that the proposed approach is superior to a number of alternative approaches.

Efficient COLREG-Compliant Collision Avoidance in Multi-Ship Encounter Situations

Abstract: Ship collisions are major types of maritime accidents which may involve the loss of life and significant damage to property and the environment. Although many automatic ship collision avoidance algorithms have been suggested, most of them are only applicable to a single ship-to-ship encounter situation. Also, although there exist some studies on collision avoidance for multiple agent systems, maritime traffic rules have not been systematically incorporated in the algorithms which limit their practical applicability to real maritime traffic situations. In this study, we propose a rule-compliant automatic ship collision avoidance method that can be applied not only to single ship-to ship situations, but also to multiple-ship encounter situations with consideration of prediction uncertainty. In order to select appropriate evasive actions, a symmetric role-classification criterion is proposed by refining the current maritime traffic rules, and an efficient collision avoidance algorithm based on the probabilistic velocity obstacle method is applied. To verify and demonstrate the performance and practical utility of the proposed algorithm, Monte-Carlo simulations were conducted and the results are presented in this article.

Multi-UAV Collaborative Transportation of Payloads With Obstacle Avoidance

Abstract: Control barrier functions have been widely studied and applied to safety-critical systems, including multi-agent obstacle avoidance problems. In this work, we apply control barrier functions to a collaborative transportation problem involving two unmanned aerial vehicles (UAVs) moving a payload around obstacles as they deliver it to a target location. We develop a target-tracking controller for the UAVs, which is constrained to meet the requirements of payload dynamics and obstacle avoidance. We also present simulation results to demonstrate the benefits of the proposed problem formulation for a multi-obstacle environment.

Optimization Methods Applied to Motion Planning of Unmanned Aerial Vehicles: A Review

Abstract: A system that can fly off and touches down to execute particular tasks is a flying robot. Nowadays, these flying robots are capable of flying without human control and make decisions according to the situation with the help of onboard sensors and controllers. Among flying robots, Unmanned Aerial Vehicles (UAVs) are highly attractive and applicable for military and civilian purposes. These applications require motion planning of UAVs along with collision avoidance protocols to get better robustness and a faster convergence rate to meet the target. Further, the optimization algorithm improves the performance of the system and minimizes the convergence error. In this survey, diverse scholarly articles were gathered to highlight the motion planning for UAVs that use bio-inspired algorithms. This study will assist researchers in understanding the latest work done in the motion planning of UAVs through various optimization techniques. Moreover, this review presents the contributions and limitations of every article to show the effectiveness of the proposed work.

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

Abstract: This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Abstract: Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called Highlight Experience Replay and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages.

Multigoal Visual Navigation With Collision Avoidance via Deep Reinforcement Learning

Abstract: Learning to map the images acquired by a moving agent equipped with a camera sensor to motion commands for multigoal navigation is challenging. Most existing approaches are still struggling against collision avoidance, faster convergence, and generalization. In this article, a novel actor–critic architecture is presented to learn the optimal navigation policy. We introduce single-step reward observation and collision penalty to reshape the reinforcement learning (RL) reward function. The collision perception can be obtained by the reshaped reward function and treated as measurement information from the visual observation to avoid obstacles. Besides, expert trajectories are used to generate subgoals. A subgoal reward shaping is then proposed to accelerate policy learning with the expert knowledge of subgoals. In order to generate human-aware navigation policies, an observation-action consistency (OAC) model is introduced to ensure that the agent reaches the subgoals in turn, and moves toward the target. The whole training process is performed on a self-supervised RL approach, accompanied by an expert supervision signal. This method balances the exploration and exploitation, helping the proposed model to generalize to unseen goals. The training experiments on AI2-THOR show better performance and faster convergence speed, compared with the existing approaches. For the generalization capacity to unseen goals, the proposed method achieves the state-of-the-art success rate, with at least a 30% improvement of average episode collision.

A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication

Abstract: Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This article investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system,which can obtain the maximum ground friction through vehicle-road data, and provide key predictive parameters for multi-vehicle safety controller. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this article can avoid or mitigate the collision risk compared to the vehicle without this system.