Motion planning of autonomous mobile robots by iterative dynamic programming

Abstract

We propose a new offline motion planning method for autonomous mobile robots. To minimize traveling time, a smooth path and a time-optimal velocity profile should be generated under kinematic and dynamic constraints. In this study, we develop an effective and practical method to generate a good solution with lower computation time. The initial path is obtained from a Voronoi diagram and spline function, and is improved by iteratively changing via-points. We apply a dynamic programming algorithm to change the via-points, and a Hermite interpolation to generate a smooth trajectory. Simulation results are presented to verify the performance of the proposed method.

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

“…If environment parameters are unknown or highly uncertain then local planning is performed, also called online (sensor based, or reactive). Whereas, a known environment requires global planning, also called offline (map based) [23,54]. Further, RRT* variants based on bidirectional trees also exist in literature, which generate two trees simultaneously from start and goal states.…”

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

“…The dynamic programming method is a commonly used method for solving optimal problems, which decomposes originally complex problems into multiple subproblems that are relatively easy to solve. By solving subproblems and gradually recursively obtaining the global optimal solution, complex problems such as graphic search and network flow can be effectively solved, avoiding repetitive calculations, saving time and memory space, and reducing time complexity when solving certain problems [20][21][22][23]. However, due to the need to save and update the optimal solution of the subproblem, this algorithm requires additional space for storage, which can occupy a considerable amount of storage space when the problem is relatively complex.…”

Rolling Biped Polynomial Motion Planning

“…Alternatively, when there is only robot's local sensor data and the whole environment is unknown, it uses feedback from local observations. Planning methods can be generalized into four types: deterministic (based on mathematical numeric/analytic functions and models) [1][2][3], stochastic (recursive numerical methods based on probabilistic uncertainties) [4][5][6], heuristic (algorithms based on logical control and humanheuristic decision-making) [7][8][9][10], and mixed planning methods [11][12][13].…”