Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
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.
Code (1)
Tasks
Evolutionary AlgorithmsSimilar Papers 제목 키워드 기반
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer…
Energy Aware and Safe Path Planning for Unmanned Aircraft Systems
This paper proposes a path planning algorithm for multi-agent unmanned aircraft systems (UASs) to autonomously cover a search area, while considering obstacle avoidance, as well as the capabilities and energy consumption…
Model Predictive ControlGenetic Algorithm Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Cell-Grid Environments
Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of m…
Toward Training Superintelligent Software Agents through Self-Play SWE-RL
While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.g., GitHub issues and pull requests) and environment…
Reinforcement LearningInterference Aware Path Planning for Mobile Robots in mmWave Multi Cell Networks
The emerging beyond 5G and envisioned 6G wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper…