Practical Motion Planning in Unknown and Unpredictable Environments

Motion planners for robots in unknown and dynamic environments often assume known obstacle geometry and use that to predict unknown motions of obstacles through tracking, but such an assumption may not be realistic. In [1], we introduced a collision-free perceiver (CFP) that can detect guaranteed collision-free trajectory segments in the unknown configuration-time (CT) space of a robot without assuming known obstacle geometry or motion. However, such a guarantee by the CFP is at the expense of a finite period for perception and processing of each collision-free CT point. In this paper, we address how to incorporate the CFP, taking into account its finite processing time, into real-time motion planning to enable a robot of high degree of freedom to plan and move at the same time in an unknown and unpredictable environment while minimizing unsafe stops when the robot may collide with an obstacle. The approach was implemented and tested in experiments with a real 7-DOF robot arm and a stereo-vision sensor, indicating the potential of the approach.