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A novel decentralised trajectory generation algorithm for Multi Agent systemsis presented. Multi-robot systems have the capacity to transform lives in avariety of fields. But, trajectory generation for multi-robot systems is stillin its nascent stage and limited to heavily controlled environments. Toovercome that, an online trajectory optimization algorithm that generatescollision-free trajectories for robots, when given initial state and desiredend pose, is proposed. It utilizes a simple method for obstacle detection,local shape based maps for obstacles and communication of robots' currentstates. Using the local maps, safe regions are formulated. Based upon thecommunicated data, trajectories are predicted for other robots and incorporatedfor collision-avoidance by resizing the regions of free space that the robotcan be in without colliding. A trajectory is then optimized constraining therobot to remain within the safe region with the trajectories represented bypiecewise polynomials parameterized by time. The algorithm is implemented usinga receding horizon principle. The proposed algorithm is extensively tested insimulations on Gazebo using ROS with fourth order differentially flat aerialrobots and non-holonomic second order wheeled robots in structured andunstructured environments.

Online Decentralized Receding Horizon Trajectory Optimization for Multi-Robot systems