McCullough, Shaun | Engineering Papers

NASA NTRS1998

Multiobjective Evolutionary Path Planning via Sugeno-Based Tournament Selection

This paper introduces a new tournament selection algorithm that can be used for evolutionary path planning systems. The fuzzy (Sugeno) tournament selection algorithm (STSA) described in this paper selects candidate paths (CPs) to be parents and undergo reproduction based on: (1) path feasibility, (2) the euclidean distance of a path from the origin to its destination, and (3) the average change in the slope of a path. In this paper, we provide a detailed description of the fuzzy inference system used in the STSA as well as some examples of its usefulness. We then use 12 instances of our STSA to rank a population of CPs based on the above criteria. We also show how the STSA can obviate the need for the development of an explicit (lexicographic multiobjective) evaluation function and use it to develop multiobjective motion paths.

Dozier, Gerry↗

NASA NTRS1997

Hybrid Co-Evolutionary Motion Planning via Visibility-Based Repair

This paper introduces a hybrid co-evolutionary system for global motion planning within unstructured environments. This system combines the concept of co-evolutionary search along with a concept that we refer to as the visibility-based repair to form a hybrid which quickly transforms infeasible motions into feasible ones. Also, this system makes use of a novel representation scheme for the obstacles within an environment. Our hybrid evolutionary system differs from other evolutionary motion planners in that (1) more emphasis is placed on repairing infeasible motions to develop feasible motions rather than using simulated evolution exclusively as a means of discovering feasible motions, (2) a continuous map of the environment is used rather than a discretized map, and (3) it develops global motion plans for multiple mobile destinations by co-evolving populations of sub-global motion plans. In this paper, we demonstrate the effectiveness of this system by using it to solve two challenging motion planning problems where multiple targets try to move away from a point robot.

Dozier, Gerry↗