Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning
This addresses the challenge of enabling multi-robot systems to operate safely in dynamic, unknown settings, which is incremental as it builds on prior work by extending trajectory planning to unknown environments.
The paper tackles the problem of generating collision-free trajectories for multiple robots in unknown environments by developing an online trajectory optimization algorithm that uses communication of robot states and local obstacle maps to predict trajectories and define safe regions, and it is tested in simulations on Gazebo using ROS.
Multi robot systems have the potential to be utilized in a variety of applications. In most of the previous works, the trajectory generation for multi robot systems is implemented in known environments. To overcome that we present an online trajectory optimization algorithm that utilizes communication of robots' current states to account to the other robots while using local object based maps for identifying obstacles. Based upon this data, we predict the trajectory expected to be traversed by the robots and utilize that to avoid collisions by formulating regions of free space that the robot can be without colliding with other robots and obstacles. A trajectory is optimized constraining the robot to remain within this region.The proposed method is tested in simulations on Gazebo using ROS.