- Title
- Depth-based sampling and steering constraints for memoryless local planners
- Creator
- Nguyen, Binh; Nguyen, Linh; Choudhury, Tanveer; Keogh, Kathleen; Murshed, Manzur
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198881
- Identifier
- vital:19119
- Identifier
-
https://doi.org/10.1007/s10846-023-01971-7
- Identifier
- ISSN:0921-0296 (ISSN)
- Abstract
- By utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach. © 2023, The Author(s).
- Publisher
- Institute for Ionics
- Relation
- Journal of Intelligent and Robotic Systems: Theory and Applications Vol. 109, no. 3 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023, The Author(s)
- Rights
- Open Access
- Subject
- 4007 Control engineering, mechatronics and robotics; 4602 Artificial intelligence; Depth image; Local planning; Quadrotor; Sampling; Steering
- Full Text
- Reviewed
- Funder
- Open Access funding enabled and organized by CAUL and its Member Institutions. Federation University Australia funded the research via the Henry Sutton scholarship- Application ID: 3056759.
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