Abstract:
Often times in mobile robotics, optimizing a sequence of tasks and the paths between those destinations is an essential factor. In simple cases, this problem can be model...Show MoreMetadata
Abstract:
Often times in mobile robotics, optimizing a sequence of tasks and the paths between those destinations is an essential factor. In simple cases, this problem can be modeled by the well-researched Traveling Salesman Problem (TSP). In more complex situations however, the TSP is not a suitable model. In redundant robotic systems, a robot can assume infinitely many configurations while performing each given task. In these cases, not only is it necessary to optimize the sequence of tasks, but an optimal configuration must be defined for each task as well. This optimization problem can be better modeled by the Traveling Salesman Problem with Neighborhoods (TSPN) in which nodes can move in given domains called neighborhoods. However, one of the limiting factors to the TSPN is that it cannot efficiently solve realistic instances with non-connected neighborhoods. This research proposes an approach to this problem in which the TSPN is extended into a Generalized Traveling Salesman Problem with Neighborhoods (GTSPN) where each node can be located in multiple regions, called neighborhoodsets. An heuristic procedure is proposed to find a near-optimal tour for GTSPN instances using a genetic algorithm approach. Numerical simulations performed on randomly generated instances with up to 300 neighborhoods show that the proposed procedure determines tours for a given instance within a 1% standard deviation error from the best-known tour.
Date of Conference: 14-18 September 2014
Date Added to IEEE Xplore: 06 November 2014
ISBN Information: