Cycle time based multi-goal path optimization for redundant robotic systems

Finding an optimal path for a redundant robotic system to visit a sequence of several goal placements poses two technical challenges. First, while searching for an optimal sequence, infinitely many feasible configurations can be used to reach each goal placement. Second, obstacle avoidance has to be considered while optimizing the path from one goal placement to the next. Previous works focused on solving a discrete formulation of this optimization problem where only few configurations are used to represent each goal placement. We instead model it as a Traveling Salesman Problem with Neighborhoods (TSPN), where each neighborhood is defined as the set of the infinitely many configurations corresponding to the same goal placement. A solution procedure based on a Hybrid Random-key Genetic Algorithm (HRKGA) and bidirectional Rapidly-exploring Random Trees (biRRTs) is then proposed. Finally, experimental tests performed on a 7-Degree Of Freedom (DOF) industrial vision inspection system show that the proposed method is able to drastically reduce the cycle time currently required by the system.