Motion Planning of Mobile Robots for Autonomous Navigation on Uneven Ground Surfaces

With the increasing interest in mobile robots for construction applications, autonomous navigation in unstructured and uneven construction sites has become a critical challenge. To ensure the safe and robust navigation of a robot in such environments, this study develops an optimal obstacle-avoiding path planner for stable posture (OOPS) that minimizes the distance to the goal area and stabilizes the posture of the robot. In this study, a new algorithm was developed by significantly improving the quick-rapidly exploring random tree* (Q-RRT*) algorithm to find a feasible path and converge to the optimal path more quickly. A cost function was defined to stabilize the posture of the robot. A simulation experiment was conducted to examine the feasibility of the OOPS algorithm, and its performance was compared with those of other algorithms. The OOPS algorithm was also validated in an experiment in a real-world outdoor environment with sloped hills and several obstacles. The results demonstrate that the OOPS algorithm outperforms other algorithms in terms of the time needed to find the initial solution, time to convergence on the optimal solution, and rate of success in reaching the goal. A mobile robot with the OOPS algorithm is able to start navigating sooner, and the algorithm takes less time than other algorithms to produce paths that are closer to the optimal path. The robot's posture is more stable when it follows the path obtained from the OOPS algorithm in both simulation and real-world tests. Therefore, the OOPS algorithm can be effectively used for applications in uneven, highly sloped, and unstructured outdoor environments.