The Autonomous Route Planning Algorithm for Rock Drilling Manipulator Based on Collision Detection
ABSTRACT In the field of high‐redundancy manipulators, specifically in the rock drilling manipulator domain, fast and efficient path planning is crucial. Therefore, this paper proposes an improved algorithm, v‐BI‐RRT, based on the BI‐RRT algorithm and oriented vector methods. In this algorithm, the nodes along one path are extended in the direction of the node coordinates of another path as the target direction. When the path collides with an obstacle, new node coordinates are generated using a random sampling method to bypass the obstacle. This approach enhances spatial search efficiency. For high‐redundancy manipulators like the rock drilling manipulator, self‐collision avoidance is a key component of collision‐free path planning. This paper uses oriented bounding boxes (OBB) and capsules to envelope the manipulator's body. Potential self‐collisions are detected in two stages: during the rapid detection phase, non‐colliding pairs are quickly excluded, and during the precise detection phase, the distance between the remaining potential collision pairs is calculated using Euclidean distance to find the shortest distance. Finally, the self‐collision detection algorithm is integrated into the v‐BI‐RRT algorithm. Simulations and experiments demonstrate that the algorithm responds quickly and performs well in avoiding collisions when applied to path planning for the rock drilling manipulator.
Authors
- Dianzheng Wang (ORCID: https://orcid.org/0000-0001-6382-9009)
- Yichao Li (ORCID: https://orcid.org/0000-0002-3384-6614)
- Bo Chen (ORCID: https://orcid.org/0000-0002-3833-9670)
- Yundou Xu (ORCID: https://orcid.org/0000-0001-5578-6678)
- Shenglong Nie
Institutions
- Yanshan University (CN)
Publication Details
- Journal
- Journal of Field Robotics
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1002/rob.22596
- Citations
- 1
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 1.28