Continuous-Time Trajectory Optimization for Decentralized Multi-Robot Navigation
This addresses the problem of safe, efficient navigation for multi-robot systems in fields like logistics or surveillance, though it appears incremental as it builds on existing trajectory optimization methods.
The paper tackles decentralized collision-free navigation for high-speed multi-robot systems by developing an online replanning algorithm that uses continuous-time trajectory optimization, tested in simulations with aerial robots.
Multi-robot systems have begun to permeate into a variety of different fields, but collision-free navigation in a decentralized manner is still an arduous task. Typically, the navigation of high speed multi-robot systems demands replanning of trajectories to avoid collisions with one another. This paper presents an online replanning algorithm for trajectory optimization in labeled multi-robot scenarios. With reliable communication of states among robots, each robot predicts a smooth continuous-time trajectory for every other remaining robots. Based on the knowledge of these predicted trajectories, each robot then plans a collision-free trajectory for itself. The collision-free trajectory optimization problem is cast as a non linear program (NLP) by exploiting polynomial based trajectory generation. The algorithm was tested in simulations on Gazebo with aerial robots.