A new local path planning approach based on improved dual covariant Hamiltonian optimization for motion planning method

We propose a new local path planning approach based on optimization methods with probabilistic completeness in this article. This approach adds a linear constraint to the original covariant Hamiltonian optimization for motion planning problem with a new cost function. By deducing the dual form, the path planning problem is described as a box-constrained quadratic programming problem. The nonmonotone gradient projection algorithm is introduced to solve the dual problem, which makes the algorithm adaptable to non-convex cost functions. In order to prevent early convergence at local minima that can occur when applying optimization methods, this article introduces Hamiltonian Monte Carlo to the modification, which constantly forces the initial path to jump out of the local extremum, thus improving the robustness and success rate of the path planning approach. Compared with other methods through simulations, this approach is proven to provide balanced planning efficiency and path quality. The feasibility in a real environment is experimentally validated by applying the approach to a wheeled mobile robot.