Description
Addressing the combined task and motion-planning problem is becoming increasingly important as a growing number of diverse robotics applications in navigation, search-and-rescue, manipulation, and surgical procedures involve reasoning with both discrete actions and continuous motions. Towards this end I perform research that brings together aspects from both the AI and Robotics communities into a framework that couples sampling-based motion planning with discrete solvers to generate dynamically-feasible collision free trajectories while considering high level task specifications (via. Regular Languages, Linear Temporal Logic, etc.).
Project Aims
- Reduce the cognitive workload of human operators by adapting high-level planning formalisms to form the basis of a mission specification language
- Enhance the autonomy by constructing a hybrid planning framework that combines high-level planning with sampling based motion planning
- Develop a means for the robot to recover and adapt to changing contextual conditions by developing robust replanning capabilities