Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles

Al-Baghdadi, Ahmed; Lian, Xiang; Cheng, En

doi:10.1016/j.is.2019.101453

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Although the background environment of these applications is simple, but they provide a feasible concept for the detection of buildings and their safety assessment under demanding environments. The difficulty in the post-earthquake environment is how the robot adapting complex shapes and irregularly distributed obstacles (Mucka, 2013 ; Al-Baghdadi et al, 2020 ; Luo et al, 2020 ; Su et al, 2020 ). It is obvious that the traditional path planning methods are difficult to meet this harsh condition.…”

Section: Introductionmentioning

confidence: 99%

Distributed Small-Step Path Planning and Detection Method for Post-earthquake Robot to Inspect and Evaluate Building Damage

Tang

Wang

et al. 2022

Front. Neurorobot.

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Post-earthquake robots can be used extensively to inspect and evaluate building damage for safety assessment. However, the surrounding environment and path for such robots are complex and unstable with unexpected obstacles. Thus, path planning for such robot is crucial to guarantee satisfactory inspection and evaluation while approaching the ideal position. To achieve this goal, we proposed a distributed small-step path planning method using modified reinforcement learning (MRL). Limited distance and 12 directions were gridly refined for the robot to move around. The small moving step ensures the path planning to be optimal in a neighboring safe region. The MRL updates the direction and adjusts the path to avoid unknown disturbances. After finding the best inspection angle, the camera on the robot can capture the picture clearly, thereby improving the detection capability. Furthermore, the corner point detection method of buildings was improved using the Harris algorithm to enhance the detection accuracy. An experimental simulation platform was established to verify the designed robot, path planning method, and overall detection performance. Based on the proposed evaluation index, the post-earthquake building damage was inspected with high accuracy of up to 98%, i.e., 20% higher than traditional unplanned detection. The proposed robot can be used to explore unknown environments, especially in hazardous conditions unsuitable for humans.

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Section: Introductionmentioning

confidence: 99%