In this thesis, we propose a general solution to the rearrangement of multiple
arbitrarily-shaped objects on a cluttered flat surface with multiple movable objects and
obstacles. In particular, we introduce a novel method to solve the object placement problem,
utilizing nested local searches guided by intelligent heuristics to efficiently perform
multi-objective optimizations. The solutions computed by our method satisfy the
collision-freeness constraint, and involves minimal movements of the clutter. Based on such a
solution, we introduce a hybrid method to generate an optimal feasible rearrangement plan, by
integrating ASP-based high-level task planning with low-level feasibility checks. Our hybrid
planner is capable of solving challenging non-monotone rearrangement planning instances that
cannot be solved by the existing geometric rearrangement approaches.
Several tools to operate theBaxterrobot using its cameras to identify, localize
and provide visual servoing for pick-and-place operations. Also allows communication with the
Bullet physics engine via ROS to generate the environment it sees in live simulation.