A Heuristic Path Planning Algorithm for Inspection Robots

Tang, Qichao; Ma, Lei; Sun, Yongkui; Yang, Guang; Li, Zhongfa; Zhao, Dan

doi:10.23919/ccc50068.2020.9188455

Cited by 8 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analytical examination of the high-voltage power equipment found in substations has theoretical and technological implications. In a study by Tang et al (2020), a new navigation system was developed, and the Robot Operating System (ROS) was used to create a simulation environment for the chassis. The data from the adequate amount and inertial navigation system were combined using an Extended Kalman Filter (EKF).…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Tang,

Xue,

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%