Task-Motion Planning for Navigation in Belief Space
This addresses the challenge of autonomous robot navigation in complex, uncertain scenarios, but it appears incremental as it builds on existing task-motion planning concepts.
The paper tackles the problem of integrated task and motion planning for robots navigating in large-scale environments with uncertainty, presenting a probabilistically complete framework that returns task-level optimal plans and validates it in a simulated office environment.
We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge intensive domains, on the one hand, a robot has to reason at the highest-level, for example the regions to navigate to; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. Furthermore, our framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated with a simulated office environment in Gazebo. In addition, we discuss the limitations and provide suggestions for improvements and future work.