Path planning is a key technology in robot navigation and has long attracted significant attention. However, in scenarios with high-density or unstructured obstacle distributions, path planning methods based on swarm intelligence optimization still face issues of low computational efficiency and poor path quality, limiting their performance in real-time applications. To address these challenges, this paper defines path key points and proposes a path planning method based on the Key-Points Encoding Genetic Algorithm (KEGA). First, an encoding scheme is designed to map key-point sequences into binary encodings, guiding the population to explore efficiently. Then, a new path generation module is integrated using target point direction, local environment, and historical path information to generate high-quality key-point sequences, thereby improving path quality. Additionally, by evaluating key-point sequences as a proxy for full path evaluation, only one precise path construction is required per iteration, significantly reducing computational overhead. Experiments were conducted on four simulated maps with diverse obstacle distribution characteristics and eight real-world street maps to validate the method’s robustness and generalizability. The results show that, compared to the existing state-of-the-art robot path planning methods, the proposed method achieves an average runtime savings of 75.40%, a path length reduction of 35.65% and a path smoothness improvement of 68%.
Yang et al. (2026) studied this question.