Although one of the fundamental problems in robotics, the motion planning problem is inherently hard from a computational point of view. In particular, the piano movers’ problem [1], [2] is known to be PSPACE-hard, which implies that any algorithm aimed to solve this problem (with completeness guarantees) is expected not to scale well with increasing number of dimensions of the configuration sp...

We present a plan representation and a generalized algorithm template, called UCP, for unifying the classical plan-space and state-space planning approaches within a single framework. UCP models planning as a process of refining a partial plan. The plan-space and state-space planning approaches are cast as complementary refinement strategies operating on the same partial plan representation. UC...

One of the most successful approaches in automated planning is to use heuristic state-space search. A popular heuristic that is used by a number of state-space planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, however, this relaxation produces rather weak estimates to guide search effectively. We present a relaxation usin...

There is a need for highly redundant manipulators to work in complex, cluttered environments. We explore kinematics and path planning for highly redundant manipulators by means of a continuous manipulator model, which captures the macroscopic shape of highly redundant manipulators. A path for the continuous manipulator model can be found by nding a smooth path for its end-e ector under a maximu...

There is a need for highly redundant manipulators to work in complex, cluttered environments. We explore kinematics and path planning for highly redundant manipulators by means of a continuous manipulator model, which captures the macroscopic shape of highly redundant manipulators. A path for the continuous manipulator model can be found by nding a smooth path for its end-e ector under a maximu...