Distributed Task Allocation and Task Sequencing for Robots with Motion Constraints
This thesis considers two routing and scheduling problems. The first problem is task allocation and sequencing for multiple robots with differential motion constraints. Each task is defined as visiting a point in a subset of the robot configuration space this definition captures a variety of tasks including inspection and servicing, as well as onein a set tasks. Our approach is to transform the problem into a multi vehicle generalized traveling salesman problem (GTSP) We analyze the GTSP insertion methods presented in [1] and we provide bounds on the performance of the three insertion mechanisms. We then develop a combinatorial auction based distributed implementation of the allocation and sequencing algorithm. The number of the bids in a combinatorial auction, a crucial factor in the runtime, is shown to be linear in the size of the tasks. Finally, we present extensive benchmarking results to demonstrate the improvement over existing distributed task allocation methods. In the second part of this thesis, we address the problem of computing optimal paths through three consecutive points for the curvature constrained forward moving Dubins vehicle. Given initial and final configurations of the Dubins vehicle, and a midpoint with an unconstrained heading, the objective is to compute the midpoint heading that minimizes the total Dubins path length. We provide a novel geometrical analysis of the optimal path, and establish new properties of the optimal Dubins' path through three points. We then show how our method can be used to quickly refine Dubins TSP tours produced using state of the art techniques. We also provide extensive simulation results showing the improvement of the proposed approach in both runtime and solution quality over the conventional method of uniform discretization of the heading at the mid point, followed by solving the minimum Dubins path for each discrete heading.