Multi-source term estimation based on parallel particle filtering and dynamic state space in unknown radiation environments

Bai, Hua; Du, Zhijiang; Zhu, Hongbiao; Ding, Pengchao; Wang, Gongcheng; Wang, Han; Xu, Wei; Wang, Weidong

doi:10.1016/j.buildenv.2023.110281

Cited by 4 publications

(1 citation statement)

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“…Mobile robots equipped for obstacle negotiation are commonly used in applications such as rescue and disaster relief (Wang et al, 2023;Bai et al, 2023aBai et al, , 2023b, scientific exploration (Zhu et al, 2021) and military operations due to their robust environmental adaptability (Bai et al, 2023a(Bai et al, , 2023b. Obstacle negotiation is the premise for robots to overcome obstacles (Zhu et al, 2021), and the methods for mobile robots to achieve autonomous obstacle negotiation can be divided into two categories: environment-based motion planning obstacle negotiation method (Li et al, 2016) and information-based online control obstacle negotiation method (Vincent and Sun, 2012;Sokolov et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Towards an obstacle detection system for robot obstacle negotiation

Wang,

Zhang,

Fan

et al. 2024

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Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

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