Path Tracking with Obstacle Avoidance for Pseudo-Omnidirectional Mobile Robots Using Convex Optimization

We consider the problem of trajectory generation for time-optimal path tracking for the class of pseudo-omnidirectional mobile robots. An Euler-Lagrange model of the robot dynamics is derived, and by writing it on special form a convex reformulation of the path-tracking problem can be utilized. This enables the use and regeneration of time-optimal trajectories during runtime. The proposed approach also incorporates avoidance of moving obstacles, which are unknown a priori. Using sensor data, objects along the desired path are detected. Subsequently, a new path is planned and the corresponding time-optimal trajectory is found. The robustness of the method and its sensitivity to model errors are analyzed and discussed with extensive simulation results. Moreover, we verify the approach by successful execution on a physical setup.