Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
This addresses the issue of path planning failure in new scenarios for unmanned aerial vehicles, representing an incremental improvement in adaptive algorithm design.
The paper tackles the problem of evolutionary algorithms being too sensitive to environmental changes for UAV path planning by proposing the Evolutionary Programmer, which recomposes operators to create adaptive planners, resulting in improved adaptability without specific numerical gains mentioned.
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.