Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms

TL;DR

This paper introduces Evolutionary Programmer, a machine learning approach that automatically recomposes evolutionary algorithm operators to adapt path planning programs for UAVs to changing environments.

Contribution

It presents a novel method that decomposes and recomposes evolutionary algorithm operators to create adaptable path planning programs, addressing environmental sensitivity issues.

Findings

Successfully generated adaptable path planners for UAVs.

Improved planner performance across diverse scenarios.

Demonstrated effectiveness of operator recomposition approach.

Abstract

Evolutionary algorithms are wildly used in unmanned aerial vehicle path planning for their flexibility and effectiveness. Nevertheless, they are so sensitive to the change of environment that can't adapt to all scenarios. Due to this drawback, the previously successful planner frequently fail in a new scene. In this paper, a first-of-its-kind machine learning method named Evolutionary Programmer is proposed to solve this problem. Concretely, the most commonly used Evolutionary Algorithms are decomposed into a series of operators, which constitute the operator library of the system. The new method recompose the operators to a integrated planner, thus, the most suitable operators can be selected for adapting to the changing circumstances. Different from normal machine programmers, this method focuses on a specific task with high-level integrated instructions and thus alleviate the problem of huge search space caused by the briefness of instructions. On this basis, a 64-bit sequence is presented to represent path planner and then evolved with the modified Genetic Algorithm. Finally, the most suitable planner is created by utilizing the information of the previous planner and various randomly generated ones.