Abstract
Path planning plays a significant role in robot navigation applications, as path exploration ability requires the knowledge of both the kinematics and the environments. Most of the current methods consider the planning process alone instead of combining the planning results with tracking control, which leads to a significant reduction in the availability of the path, especially in complex scenarios with missing GPS and low positioning sensor accuracy. This paper proposes a reinforcement learning-based path planning algorithm, which aims to consider the errors caused by the robot’s motion during the dead-reckoning process and effectively reduces the cumulative error within the optimization process. The simulation conclusion in the 2D scene verifies the effectiveness of the algorithm for reducing the cumulative error.
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This work was supported by the National Natural Science Foundation of China (NSFC) under Grans 61703335.
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Wang, C., Cheng, C., Yang, D., Zhang, F., Pan, G. (2021). Path Planning and Simulation Based on Cumulative Error Estimation. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_12
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DOI: https://doi.org/10.1007/978-981-16-2336-3_12
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