Abstract
When guiding vision-impaired individuals, quadruped robots must promptly avoid obstacles and crowds and follow a safe path. Though reinforcement learning approaches are widely adopted for obstacle avoidance and navigation in complex and unknown environments, most of them suffer from low learning efficiency. This work proposes an interaction of hindsight experience replay (HER), intermediate waypoints, and a direction-aware reward function to tackle reward sparsity and learning inefficiency. Training results in the simulation environment demonstrate that the proposed algorithm significantly increases both success rate and reward compared to the Twin Delayed Deep Policy Gradient (TD3) algorithm. Furthermore, test results on the trained model indicate that the proposed algorithm performs better than TD3 in the same environment. This work enables quadruped robots to perform obstacle-avoidance navigation in complex and crowded environments, reinforcing their safety in guiding vision-impaired individuals.
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Acknowledgments
This work is supported by the project of National Natural Science Foundation of China (No.62373285) and the Key Pre-Research Project of the 14th-Five-Year-Plan on Common Technology. Meanwhile, this work is also partially supported by the “National High Level Overseas Talent Plan” project and the “National Major Talent Plan” project (No. 2022-XXXX-XXX-079). It is also partially sponsored by the fundamental research project (No. XXXX2022YYYC133), the Shanghai Industrial Collaborative Innovation Project (Industrial Development Category, No. HCXBCY-2022-051), as well as the project of Space Structure and Mechanism Technology Laboratory of China Aerospace Science and Technology Group Co. Ltd (No. YY-F805202210015). All these supports are highly appreciated.
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Li, X., Chen, F., Wang, Y., Ma, B., Tang, Q. (2025). Obstacle Avoidance for Guided Quadruped Robots in Complex Environments. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15209. Springer, Singapore. https://doi.org/10.1007/978-981-96-0789-1_8
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DOI: https://doi.org/10.1007/978-981-96-0789-1_8
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