Self-Organizing Cooperative Pursuit Strategy for Multi-USV with Dynamic Obstacle Ships

A self-organizing cooperation strategy for multiple unmanned surface vessels (USVs) to pursue intelligent evaders in the case of a dynamic obstacle vessel is proposed. Firstly, a self-organizing cooperative hunting strategy is proposed to form an Apollonius circle. According to the escape strategies of evaders under different encirclement states, the pursuers are divided into pursuit group and ambush group. The pursuit group drives the evaders into the ambush area and completes the encirclement together with the ambush group. In order to better deal with the dynamic obstacle ships encountered in the pursuit process in the dynamic ocean environment, the artificial potential field-based collision avoidance method for inter-USV and dynamic collision avoidance strategy for encountering obstacle ships based on International Regulations for Preventing Collisions at Sea (COLREGs) are proposed. The simulation results show that the algorithm can make the pursuers complete the encirclement of the evaders and has good obstacle avoidance performance and flexibility in the environment with dynamic obstacle ships.

STRING FORMATION AND OBSTACLE AVOIDANCE FOR MULTIPLE AUTONOMOUS AGENTS

A novel pursuit-based approach is presented to investigate string formation and obstacle avoidance for multiple autonomous agents. The control objective for these agents is to reach a final desired position along a collision-free trajectory. First, a line formation is achieved by using a leader-follower strategy, in which one agent is regarded as the leader and the others are followers. By using the receding horizon control, the leader agent can reach the destination while effectively avoiding collision with obstacles evolving in environment. The information on obstacles is incorporated online as they are detected by sensors with a limited detecting range. Moreover, each follower agent should only pursue its front agent. It turns out that the follower agents can keep a string formation with the leader agent wherein the string formation is useful for a fleet of agents driving in a platoon sequence on a road. The system stability can be guaranteed with a terminal-state penalty in the cost function and a terminal-state region in the optimal control problem. Finally, simulation studies are provided to verify the effectiveness of the proposed approach.

Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.