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.