Kinetic-energy-optimal and Safety-guaranteed Trajectory Planning for Bridge Inspection Robot Manipulator

Bridge inspections are essential for maintaining key infrastructure and preventing structural and functional failures. Nevertheless, traditional manual inspection techniques are plagued by laboriousness, high risk, and low efficiency. Although numerous automation inspection methods have been studied, inspection performance remains challenging. The main difficulties are redundant mechanisms, complex control, high energy consumption, and limited autonomy and safety. To address these problems, we are developing a small, lightweight, electrically-driven robotic manipulator for bridge inspection named the BIRM. Here, we propose a kinetic-energy-optimal and safety-guaranteed trajectory planning for BIRM. Compared with existing methods, it simultaneously addresses energy consumption and safety. The approach formulates a quadratic programming (QP) problem by considering the robot’s kinetic energy as the objective function, and the augmented Lagrange multiplier (ALM) is applied to find the solution of the QP. The proposed method completely satisfies the joint position, velocity, and acceleration limits at the speed level while considering collision avoidance. In this paper, the collision detection strategy can achieve low-complexity computation through several structural parameters of the bridge, thereby quickly adapting to environmental changes. Through simulation experiments, we validate the effectiveness and superiority of the proposed method. Through physical experiments, we demonstrate the sustainability and safety of bridge inspections in the field.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10802354
Citations
3
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.75

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article

Kinetic-energy-optimal and Safety-guaranteed Trajectory Planning for Bridge Inspection Robot Manipulator

Yong Chang, H. G. Wang, Tianyu Zhang, Tianlong Wang
3 citations
Robotic Path Planning Algorithms
0.75
article

Kinetic-energy-optimal and Safety-guaranteed Trajectory Planning for Bridge Inspection Robot Manipulator

Yong Chang, H. G. Wang, Tianyu Zhang, Tianlong Wang
article en
3 citations

Abstract

Bridge inspections are essential for maintaining key infrastructure and preventing structural and functional failures. Nevertheless, traditional manual inspection techniques are plagued by laboriousness, high risk, and low efficiency. Although numerous automation inspection methods have been studied, inspection performance remains challenging. The main difficulties are redundant mechanisms, complex control, high energy consumption, and limited autonomy and safety. To address these problems, we are developing a small, lightweight, electrically-driven robotic manipulator for bridge inspection named the BIRM. Here, we propose a kinetic-energy-optimal and safety-guaranteed trajectory planning for BIRM. Compared with existing methods, it simultaneously addresses energy consumption and safety. The approach formulates a quadratic programming (QP) problem by considering the robot’s kinetic energy as the objective function, and the augmented Lagrange multiplier (ALM) is applied to find the solution of the QP. The proposed method completely satisfies the joint position, velocity, and acceleration limits at the speed level while considering collision avoidance. In this paper, the collision detection strategy can achieve low-complexity computation through several structural parameters of the bridge, thereby quickly adapting to environmental changes. Through simulation experiments, we validate the effectiveness and superiority of the proposed method. Through physical experiments, we demonstrate the sustainability and safety of bridge inspections in the field.

Shenyang Institute of Automation (CN), Chinese Academy of Sciences (CN), State Key Laboratory of Robotics
Chinese Academy of Sciences, Shenyang Institute of Automation, National Key Research and Development Program of China
Openalex Percentile: Top 26%
Robotic Path Planning Algorithms
0.75
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