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Risk-Aware Path Planning Under Uncertainty in Dynamic Environments

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Abstract

This study develops a novel sampling-based path planning approach, simultaneously achieving uncertainty reduction of localization and avoidance of dynamic obstacles. The proposed path planner can generate a set of path primitives and the path selection takes into account the localization uncertainty, the collision-risk, and the cost-to-go to the target area. The weights of these quantities for selecting an optimal path are tuned adaptively by using the entropy weight method. In the process of path primitive generation, we propose an adaptive planning horizon scheme that can generate a longer path with lower localization uncertainty. Particularly, to further reduce the localization uncertainty of the path primitive, we propose a sampling strategy that is capable of biasing the sampling points to the information-rich areas. To reduce the collision-risk, we propose to calculate the probability of collision by taking the uncertainty of both the robot and the dynamic objects into consideration. The proposed approach and its key components are verified in extensive experiments in both simulation and real-world environments. The proposed method is demonstrated to be capable of efficiently guiding the robot to the designated location with lower localization uncertainty and higher success rate in obstacle avoidance.

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Funding

This work was supported the Hong Kong ITC ITSP Tier 2 grant # ITS/105/18FP: An intelligent Robotics System for Autonomous Airport Passenger Trolley Deployment, awarded to Max Q.-H. Meng.

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Authors and Affiliations

  1. Department of Electronic Engineering, The Chinese University of Hong Kong, N.T., Hong Kong, SAR, China

    Kuanqi Cai & Max Q.-H. Meng

  2. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China

    Kuanqi Cai, Shuang Song, Haoyao Chen & Max Q.-H. Meng

  3. School of Control Science and Engineering, Shandong University, Shandong, China

    Chaoqun Wang

  4. Shenzhen Key Laboratory of Robotics Perception and Intelligence, Southern University of Science and Technology, Shenzhen, China

    Max Q.-H. Meng

Authors
  1. Kuanqi Cai
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  2. Chaoqun Wang
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  3. Shuang Song
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  4. Haoyao Chen
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  5. Max Q.-H. Meng
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Contributions

The authors contributions are as follows: Conceptualization: [Kuanqi Cai], [Chaoqun Wang], [Max Q.-H. Meng]; Methodology: [Chaoqun Wang],[Kuanqi Cai]; Software: [Kuanqi Cai], [Chaoqun Wang]; Writing - original draft preparation: [Kuanqi Cai], [Chaoqun Wang]; Writing - review and editing: [Kuanqi Cai], [Chaoqun Wang]; Funding acquisition: [Shuang Song]; [Max Q.-H. Meng]; Supervision: [Shuang Song]; [Haoyao Chen], [Max Q.-H. Meng].

Corresponding author

Correspondence to Max Q.-H. Meng.

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This manuscript describes original work and is not under consideration for publication elsewhere. All authors approved the manuscript and this submission and we have no conflicts of interest to disclose.

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Kuanqi Cai and Chaoqun Wang contribute equally to this paper.

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Cai, K., Wang, C., Song, S. et al. Risk-Aware Path Planning Under Uncertainty in Dynamic Environments. J Intell Robot Syst 101, 47 (2021). https://doi.org/10.1007/s10846-021-01323-3

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