A novel algorithm is proposed for offline motion planning of a robotic arm to perform a throw task of an object to reach a goal target. The planning algorithm searches for a throw trajectory that could be performed under kinematic and dynamic (i.e., kinodynamic) constraints. We parameterize the throw trajectory by a time-invariant high-dimensional vector. Then, the kinodynamic and target constraints are formulated in terms of time and the parameterization vector. These constraints form time-varying subspaces in the parameterization space. We present a stochastic method for finding a feasible and optimal solution within the subspace. The method generates a number of random points within the parameterization space and checks their feasibility using an adaptive search. The algorithm is guaranteed under a known probability to find a solution if one exists. We present simulations and experiments on a 3R manipulator to validate the method.
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A stochastic dynamic motion planning algorithm for object-throwing
Semantic Scholar · Computer Science · 2015
Abstract
A novel algorithm is proposed for offline motion planning of a robotic arm to perform a throw task of an object to reach a goal target. The planning algorithm searches for a throw trajectory that could be performed under kinematic and dynamic (i.e., kinodynamic) constraints. We parameterize the throw trajectory by a time-invariant high-dimensional vector. Then, the kinodynamic and target constraints are formulated in terms of time and the parameterization vector. These constraints form time-varying subspaces in the parameterization space. We present a stochastic method for finding a feasible and optimal solution within the subspace. The method generates a number of random points within the parameterization space and checks their feasibility using an adaptive search. The algorithm is guaranteed under a known probability to find a solution if one exists. We present simulations and experiments on a 3R manipulator to validate the method.