Integrating Planning and Control for Efficient Path Planning in the Presence of Environmental Disturbances

Path planning for nonholonomic robots in real-life environments is a challenging problem, as the planner needs to consider the presence of obstacles, the kinematic constraints, and also the environmental disturbances (like wind and currents). In this paper, we develop a path planning algorithm called Control Based A* (CBA*), which integrates search-based planning (on grid) with a path-following controller, taking the motion constraints and external disturbances into account. We also present another algorithm called Dynamic Control Based A* (DCBA*), which improves upon CBA* by allowing the search to look beyond the immediate grid neighborhood and thus makes it more flexible and robust, especially with high resolution grids. We investigate the performance of the new planners in different environments under different wind disturbance conditions and compare the performance against (i) finding a path in the discretized grid and following it with a nonholonomic robot, and (ii) a kinodynamic sampling-based path planner. The results show that our planners perform considerably better than (i) and (ii), especially in difficult situations such as in cluttered spaces or in presence of strong winds/currents. Further, we experimentally validate the approach using a quadrotor in the outdoor environment