Trajectory planning method for pipeline installation robots based on AS-DTRRT
Clinical Snapshot
PICO Framework
| P — Population | Pipeline installation robots with 6-degree-of-freedom Cartesian coordinate robotic arms in mining environments |
| I — Intervention | AS-DTRRT (Artificial Potential Field enhanced dual-tree RRT) trajectory planning algorithm |
| C — Comparator | RRT*, D-RRT, Bi-RRT*, and A-RRT algorithms |
| O — Outcomes | Runtime efficiency, path length reduction, collision avoidance performance, and installation efficiency |
Bottom Line
This engineering study presents a novel trajectory planning algorithm (AS-DTRRT) for pipeline installation robots in mining environments, demonstrating significant improvements in efficiency and path optimisation compared to existing methods. The integration of Artificial Potential Field guidance with dual-tree RRT algorithms shows promise for enhancing robotic automation in hazardous industrial settings. While the technical advances are noteworthy, the study lacks comprehensive real-world validation and statistical rigor expected for clinical applications. The work represents an incremental but meaningful contribution to industrial robotics, particularly relevant for mining operations seeking to improve safety and efficiency through automation. However, practical implementation would require extensive field testing and adaptation to specific operational contexts before widespread adoption could be recommended.
Key Findings
P Value: Not reported
Effect Size: Runtime improvements of 4.69-20.22% and path length reductions of 6.85-21.17% compared to existing algorithms
Primary Outcome: Runtime reduction and path length optimisation for robotic trajectory planning
Nnt Or Sensitivity: Adaptive step-size strategy with collision avoidance capability demonstrated in complex mining environments
Confidence Interval: Not reported
Clinical Application
Requires specialised robotics infrastructure and technical expertise for implementation Potentially relevant to Australian mining industry given similar operational challenges, though local validation needed for specific geological and regulatory conditions Mining operations requiring automated pipeline installation in hazardous or confined spaces
Abstract
Pipeline installation robots install pipelines on both sides of mine roadways using robotic arms; the geometric shape and length of the pipelines affect the movement trajectory of the robotic arms, and the working environment is complex and changeable. Aiming at the issues of the mechanical arm's long motion trajectory, low installation efficiency, and pipeline collision vulnerability during installation, a trajectory planning method based on the improved dual-tree RRT algorithm (AS-DTRRT) is proposed. Taking the pipeline installation robot with a 6-degree-of-freedom Cartesian coordinate robotic arm as the research object, the Artificial Potential Field method is introduced to guide the growth direction of new nodes in the path of the dual-tree RRT algorithm; aiming at the problem that the Artificial Potential Field method is prone to falling into local optimality, an adaptive step-size dynamic environment bias strategy is proposed, which automatically adjusts the step size according to the environmental conditions of the position where the new node is located to further improve the path search capability. This study analyzes the influence of pipeline geometry and length on the robotic arm's working space, proposes a pipeline collision avoidance strategy considering the working environment, and applies quintic polynomial interpolation to plan the trajectory of collision-free paths while considering joint speed and acceleration constraints. Simulations and platform experiments comparing AS-DTRRT with RRT*, D-RRT, Bi-RRT*, and A-RRT validate the method's feasibility, demonstrating that AS-DTRRT outperforms the four algorithms by reducing actual runtime by 20.22%, 15.48%, 4.69%, and 16.47% and shortening average path lengths by 21.17%, 13.78%, 6.85%, and 9.24% respectively. The proposed method enhances search efficiency and reduces path lengths, providing an effective approach for trajectory planning of pipeline installation robots.
References
- 1.Liu, Z., Hu, W., & Xie, C. (2026). Trajectory planning method for pipeline installation robots based on AS-DTRRT. PLoS One. https://doi.org/10.1371/journal.pone.0346076
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