Abstract
Swarm robots often encounter dynamic obstacles when performing tasks, such as moving objects in the scene or other individuals in the robot group. The traditional passive obstacle avoidance method makes the robots take emergency avoidance behaviour when it is about to encounter obstacles. Hoverer this may destroy the group cooperation behaviour, thereby affecting the efficiency of the system. Active obstacle avoidance perceives a dynamic target and predicts the movement of the target and takes the initiative to avoid obstacles, minimizes the impact of obstacle avoidance on the system’s cooperative behaviour. Considering that the defects in the structural design of swarm robots and the avoidance strategy of swarm robots, it is necessary to focus on active obstacle avoidance of swarm robots that is based on the prediction of dynamic targets. An improved obstacle avoidance method is therefore proposed, which enables robots to avoid both static and dynamic obstacles.
This work is supported by the projects of National Natural Science Foundation of China (No. 61873192), the Quick Support Project (No. 61403110321), the Innovative Projects (No. 20-163-00-TS-009-125-01; 21-163-00-TS-011-011-01; 2021-JCJQ-LB-010-11), and the Key Pre-Research Project of the 14th-Five-Year-Plan on Common Technology (No. 50912030501). Meanwhile, this work is also partially supported by the Fundamental Research Funds for the Central Universities, as well as the project of Shanghai Key Laboratory of Spacecraft Mechanism (18DZ2272200). All these supports are highly appreciated.
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References
Huang, Y., Hu, H., Liu, X.: Obstacles avoidance of artificial potential field method with memory function in complex environment. In: 8th World Congress on Intelligent Control and Automation, Jinan, pp. 6414–6418. IEEE (2010)
Paolo, F., Zvi, S.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)
Borenstein, J., Koren, Y.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7(3), 278–288 (1991)
Ulrich, I., Borenstein, J.: VFH+: reliable obstacle avoidance for fast mobile robots. In: IEEE International Conference on Robotics and Automation, Leuven, pp. 1572–1577. IEEE (1998)
Dieter, F., Wolfram, B., Sebastian, T.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)
Saranrittichai, P., Niparnan, N., Sudsang, A.: Robust local obstacle avoidance for mobile robot based on dynamic window approach. In: 10th IEEE International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, pp. 1–4. IEEE (2013)
Zhang, Z., Zhang, P., Mao, H., Li, X., Sun, Q.: Global dynamic path planning combining improved A* algorithm and dynamic window method. Electron. Opt. Control 1–6 (2021)
Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, Pasadena, pp. 1928–1935. IEEE (2008)
Van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal \(n\)-body collision avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. STAR, vol. 70, pp. 3–19. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19457-3_1
Rudof, K.: A new approach to linear filtering and prediction problems. J. Basic Eng. Trans. 82(1), 35–45 (1960)
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Zhu, W., Cui, Y., Xu, P., Shen, Y., Tang, Q. (2022). An Active Obstacle Avoidance Method. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_31
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