Robotics and Biology Laboratory

Motion Planning in Dynamic Environments with Probalistic Connectivity Roadmaps


We present an incremental method for motion generation in environments with unpredictably moving and initially unknown obstacles. The key to the method is its incremental nature: it locally augments and adapts global motion plans in response to changes in the environment, even if they significantly change the connectivity of the world. The restriction to local changes to a global plan results from the fact that in mobile manipulation, robots can ultimately only rely on their on-board sensors to perceive changes in the world. The proposed method addresses three sub-problems of motion generation with three algorithmic components. The first component reactively adapts plans in response to small, continuous changes. The second augments the plan locally in response to connectivity changes. And the third extracts a global, goal-directed motion from the representation maintained by the first two components. In an experimental evaluation of this method, we show a real-world mobile manipulator executing a whole-body motion task in an initially unknown environment, while incrementally maintaining a plan using only on-board sensors.