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SP2E: Online Spiral Coverage with Proactive Prevention Extremum for Unknown Environments

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Abstract

Coverage path planning (CPP) is the foundation of multiple robotic applications. The efficiency of CPP is affected by the local extremum, which describes a situation with the robot surrounded by obstacles and explored areas, even if unexplored areas remain in the environment. Most online CPP methods reactively deal with the local extremum after the mobile robot is trapped within it. However, repeated coverage is generated since the path of escaping the local extremum revisits the covered areas. This paper presents an online spiral coverage framework with proactive prevention of extremum (SP2E) to address the CPP problem in an unknown environment. Unlike other CPP methods, the SP2E approach prevents the local extremum through a cut vertex detection algorithm and a direction adaptation algorithm. The cut vertex detection algorithm predicts the local extremum by detecting cut vertexes, and the direction adaptation algorithm prevents it by adjusting the spiral path’s direction. The SP2E approach was validated by simulations and real-world experiments, and its performance was compared with other CPP algorithms. The results of simulations and real-world experiments demonstrate that the SP2E approach provides the minimum coverage time and computation time while avoiding the local extremum.

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The data and code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by Science and Technology Innovation 2030 Major Project under Grant No.2020AAA0104802. The work was also supported by National Natural Science Foundation of China (Grant No. 91948303-1).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410003, China

    Lin Li, Shaowu Yang, Yaoning Lian & Hengzhu Liu

  2. Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300456, China

    Dianxi Shi & Songchang Jin

  3. Artificial Intelligence Research Center (AIRC), Defense Innovation Institute, Beijing, 100073, China

    Dianxi Shi & Songchang Jin

Authors
  1. Lin Li
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  2. Dianxi Shi
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  3. Songchang Jin
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  4. Shaowu Yang
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  5. Yaoning Lian
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  6. Hengzhu Liu
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Contributions

Conceptualization: Lin Li, Dianxi Shi, Hengzhu Liu, Shaowu Yang, and Songchang Jin; Methodology: Lin Li, and Dianxi Shi; Formal analysis and investigation: Lin Li; Writing - original draft preparation: Lin Li; Writing - review and editing: Lin Li, Yaoning Lian, and Songchang Jin.

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Correspondence to Dianxi Shi or Songchang Jin.

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Li, L., Shi, D., Jin, S. et al. SP2E: Online Spiral Coverage with Proactive Prevention Extremum for Unknown Environments. J Intell Robot Syst 108, 30 (2023). https://doi.org/10.1007/s10846-023-01844-z

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