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Learning real-time search on c-space GVDs

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Abstract

In the context of robotics, configuration space (cspace) is widely used for non-circular robots to engage tasks such as path planning, collision check, and motion planning. In many real-time applications, it is important for a robot to give a quick response to the user’s command. Therefore, a constant bound on planning time per action is severely imposed. However, existing search algorithms used in c-space gain first move lags which vary with the size of the underlying problem. Furthermore, applying real-time search algorithms on c-space maps often causes the robots being trapped within local minima. In order to solve the above mentioned problems, we extend the learning real-time search (LRTS) algorithm to search on a set of c-space generalized Voronoi diagrams (c-space GVDs), helping the robots to incrementally plan a path, to efficiently avoid local minima, and to execute fast movement. In our work, an incremental algorithm is firstly proposed to build and represent the c-space maps in Boolean vectors. Then, the method of detecting grid-based GVDs from the c-space maps is further discussed. Based on the c-space GVDs, details of the LRTS and its implementation considerations are studied. The resulting experiments and analysis show that, using LRTS to search on the c-space GVDs can 1) gain smaller and constant first move lags which is independent of the problem size; 2) gain maximal clearance from obstacles so that collision checks are much reduced; 3) avoid local minima and thus prevent the robot from visually unrealistic scratching.

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Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (Grant Nos. 61473300 and 61503402). We declare that there is no conflict of interests regarding the publication of this article. We appreciate fruitful discussion with the Sim812 group: Wei Duan, Shiguang Yue, Lin Sun, and Qi Zhang. Finally, we appreciate feedbacks from our reviewers.

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Correspondence to Quanjun Yin.

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Quanjun Yin is a professor of the College of Information System and Management, National University of Defense Technology, China. His academic interests mainly include cognitive process modeling, qualitative spatial reasoning and planning, cooperation and negotiation.

Long Qin is an assistant professor of the College of Information System and Management, National University of Defense Technology, China. His academic interests include qualitative spatial reasoning and human behavior modeling.

Yong Peng is an assistant professor of the College of Information System and Management, National University of Defense Technology, China. His academic interests include HLA/RTI, parallel and distributed simulation system.

Wei Duan received his PhD degree in control science and engineering from the National University of Defense Technology, China in 2014. His research interests include complex networks, epidemic modeling, information diffusion, agent-based simulation, social computing.

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Yin, Q., Qin, L., Peng, Y. et al. Learning real-time search on c-space GVDs. Front. Comput. Sci. 11, 1036–1049 (2017). https://doi.org/10.1007/s11704-016-5370-4

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  • DOI: https://doi.org/10.1007/s11704-016-5370-4

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