Abstract
Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed.
Highlights
An important technique for the exploration of extraterrestrial planets during recent years is path planning.[1]
We propose a new variant of the improved Dual covariant Hamiltonian optimization for motion planning (CHOMP) with probabilistic completeness
In section ‘‘Representation of the environment,’’ we propose a procedure for establishing an energy-based map (EMAP)
Summary
An important technique for the exploration of extraterrestrial planets during recent years is path planning.[1]. Choudhury and Scherer[14] have derived a dual form of the original covariant Hamiltonian optimization for motion planning (CHOMP) method by adding a box constraint,[15] which can solve linear problems for constrained goal regions or confine the path in a specific linear search region. We improve dual covariant Hamiltonian optimization for motion planning (Dual CHOMP)[14] to function well in ill-distributed energy-based cost maps by implementing simplified non-monotone gradient projection algorithm (NGPA).[16]
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