HG-RRT*: Human-guided optimal random trees for motion planning

The paper deals with the problem of designing an RRT*-based planning algorithm that allows the user to guide the tree growth in a simple and transparent way. The key idea of the proposal is to create a planning algorithm, called HG-RRT*, that minimizes an optimization function over the configuration space where a state cost function is established. This state cost is defined as the combination of several potential fields. Each of these potential fields will attract the solution path or move it away from certain areas. The planning algorithm will try to minimize the path length, the motion effort and the variations of the cost along the path. The paper presents a description of the proposed approach as well as simulation results from a conceptual and an application example, including a thorough comparison with the TRRT planning algorithm.