Planning Robotic Manipulation with Tight Environment Constraints

In many real-world manipulation problems, the constraints imposed by the environment on an object are tight. In these cases, most state-of-the-art planners struggle to fit satisfactorily in low dimensional sub-manifolds, while still ensuring geometric and force feasibility. On the other hand, humans are at ease with such situations and indeed exploit constraints to manipulate objects proficiently. To face this challenge, we propose to merge state-of-art randomized grasp planning methods with model-based grasp analysis. We use the partial form-closure analysis framework to find the geometrically feasible motions of the object. Then, to ensure that the desired motions are physically realizable by the robot, we resort to an extension of the force-closure analysis framework accounting also for dynamic friction. We use these instruments to construct a random tree in a simplified planning space containing only object-robot configurations that are reachable through effectively applicable contact forces. The algorithm, validated in simulation and in preliminary experiments with a collaborative robot, features the ability to compute solutions for heavily constrained real-world manipulation problems.