Applying and Proving Suitable Planning Principles on RRT* for Autonomous Trucks
(2025)Department of Automatic Control
- Abstract
- This thesis investigates how high-level planning principles can be deterministically and algorithmically incorporated into a sampling-based motion planner by modifying the structure of the RRT* algorithm. In particular, the requirement to always end a planned route by a reversing maneuver can be directly imposed through the expansion and connection logic of bidirectional search-trees, rather than relying on heuristic biases or cost shaping. A modified bidirectional RRT* algorithm is proposed, in which two search trees are grown from the start and goal configurations, respectively, of a planning task, where the goal-rooted sub-tree is directionally constrained using path validation and sample rejection. These sub-trees are connected via a... (More)
- This thesis investigates how high-level planning principles can be deterministically and algorithmically incorporated into a sampling-based motion planner by modifying the structure of the RRT* algorithm. In particular, the requirement to always end a planned route by a reversing maneuver can be directly imposed through the expansion and connection logic of bidirectional search-trees, rather than relying on heuristic biases or cost shaping. A modified bidirectional RRT* algorithm is proposed, in which two search trees are grown from the start and goal configurations, respectively, of a planning task, where the goal-rooted sub-tree is directionally constrained using path validation and sample rejection. These sub-trees are connected via a pre-defined node called the waypoint node, enabling the enforcement of a fixed motion structure. Results in simulations and experiments on the real autonomous trucks verify the capability of the presented planner to reliably provide feasible paths under the stated constraints. The proposed framework provides a deterministic and modular approach to the integration of planning principles into sampling-based motionplanning algorithms for structured environments. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9212736
- author
- Feltenmark, Fabian
- supervisor
- organization
- year
- 2025
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6285
- other publication id
- 0280-5316
- language
- English
- id
- 9212736
- date added to LUP
- 2025-09-18 14:19:38
- date last changed
- 2025-09-18 14:19:38
@misc{9212736,
abstract = {{This thesis investigates how high-level planning principles can be deterministically and algorithmically incorporated into a sampling-based motion planner by modifying the structure of the RRT* algorithm. In particular, the requirement to always end a planned route by a reversing maneuver can be directly imposed through the expansion and connection logic of bidirectional search-trees, rather than relying on heuristic biases or cost shaping. A modified bidirectional RRT* algorithm is proposed, in which two search trees are grown from the start and goal configurations, respectively, of a planning task, where the goal-rooted sub-tree is directionally constrained using path validation and sample rejection. These sub-trees are connected via a pre-defined node called the waypoint node, enabling the enforcement of a fixed motion structure. Results in simulations and experiments on the real autonomous trucks verify the capability of the presented planner to reliably provide feasible paths under the stated constraints. The proposed framework provides a deterministic and modular approach to the integration of planning principles into sampling-based motionplanning algorithms for structured environments.}},
author = {{Feltenmark, Fabian}},
language = {{eng}},
note = {{Student Paper}},
title = {{Applying and Proving Suitable Planning Principles on RRT* for Autonomous Trucks}},
year = {{2025}},
}