# A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments

arXiv: https://arxiv.org/abs/2209.01482 · HTML: https://mlsanity.com/paper/2209.01482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 94 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning](https://mlsanity.com/paper/2012.09444/) (2020)
- [Conditional Generative Adversarial Networks for Optimal Path Planning](https://mlsanity.com/paper/2012.03166/) (2020)
- [Boosted Genetic Algorithm using Machine Learning for traffic control optimization](https://mlsanity.com/paper/2103.08317/) (2021)

## Built on by (most cited first, 1 of 1)

- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)

## Abstract (arXiv)

In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.