Introduction to Multi-Robot Coordination Algorithms

This passage mainly focuses on the path-planning algorithms and avoidance collision algorithms in the swarm robot field. The evolution of the path planning method of swarm robots is mentioned. It describes how the Firefly algorithm, Artificial bee colony algorithms, and Bacteria foraging optimization algorithms are derived, what theories support them to work, and the current application in a wide area. It divides collision avoidance algorithms into two parts, which are decentralized and centralized methods. Virtual Force Field, Reciprocal Velocity Obstacles (RVO), Virtual Force Field (VFF), and Optimal Reciprocal Collision Avoidance are decentralized collision avoidance solutions, and Multi-Agent Reinforcement Learning is a centralized collision avoidance algorithm. Muti-sensor-based collision avoidance is a method that uses the sensor to get the information to solve the problems. These methods are created to increase the precision and efficiency of the swarm-robot missions. For applications, these mentioned algorithms are already used in areas including air, land, and water working robot.