Multi-robot Formation Technique Based On Swarm Intelligence Algorithm Research

With the development of robot technology, the requirement for robot’s ability is higher and higher. By cooperation multiple robots may complete a task which is hard for a single robot to fulfill, the research on multi-robot receives attention increasingly. Formation control is an important research direction of multi-robot technology. In many applications, robots must form and maintain formation to complete complex tasks. Multi-robot formation is took as research object, the main works are as follows:Firstly, for solving formation problem in known static environment, a GACO-based multi-robot formation global path planning algorithm is proposed. The grids correlation matrix is designed in order to avoid collisions between robots and static obstacles, and it reduces the amount of computation. Multi-robot can select the optimal paths quickly based on cost function that is made of formation errors and paths length. In addition, unknown dynamic obstacles are added in the above environment, keeping formation problem is studied. At first, collisions between multi-robot and dynamic obstacles’trajectories are predicted in order to avoid the obstacles. And then, according to the avoiding dynamic obstacles strategy, local optimal paths are planned. Simulation results show that the algorithm has good path planning adaptability.Secondly, for completing forming formation task in unknown static environment, a PSO-based multi-robot forming formation rolling optimization algorithm is proposed. For adapting to unknown environment, robots plan routes in a rolling way based on local environment information that is detected in real time. Through research and analysis of behavior-based, moving to destination, keeping formation, avoiding collision, avoiding obstacles penalty function, which make up a fitness function by averaging weights, are designed. Avoiding obstacles penalty function considers security and path cost requirements that makes the robot have a strong obstacle avoidance capability. PSO algorithm is used to optimize multiple robots’movement vectors in order to achieve the decision making of behaviors. In addition, in order to prevent deadlock and reduce communication, hybrid control way is used to complete this task. Simulation results show that the algorithm can form formation quickly and has good flexibility and coordination.Thirdly, for solving formation problem in unknown static and dynamic environment, a PSO-based multi-robot formation control rolling optimization algorithm is proposed. According to the environment information in rolling window, the weights of sub-goals, particles’flight direction or formation shape are adjusted dynamically, multi-robot can adapt to various unknown environment by maintaining formation, deformaing formation or changing formation. Penalty function and detour way are used in order to avoid static obstacle, it solves the problem that avoiding obstacle corner is not smooth. In addition, in order to prevent collisions between robots and the unknown dynamic obstacles, the size and trajectory of the dynamic obstacle is predicted, then handle it by using the avoiding static obstacles penalty function. Simulation results show that the algorithm has good adaptability, can avoid obstacles automatically when detecte obstacles, can restore the formation when robots are away from obstacles, and can reach the targets quckily without collisions.