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A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator.
Youyu Liu1,2,3, Wanbao Tao1,3, Shunfang Li2
1Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China.
Sensors (Basel, Switzerland)
|June 10, 2023
Summary
A new bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) algorithm enhances dual manipulator path planning. This method improves search efficiency and reduces path length, overcoming local optima in complex environments.
Area of Science:
- Robotics
- Artificial Intelligence
- Motion Planning
Background:
- Rapidly exploring random tree (RRT) algorithms are crucial for robot motion planning.
- Fixed step sizes in RRTs can lead to local optima, reducing search efficiency, especially with complex obstacles.
- Goal bias strategies improve RRT search efficiency but can still be suboptimal in certain scenarios.
Purpose of the Study:
- To propose a novel algorithm, the bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT), for efficient path planning of dual manipulators.
- To address the limitations of fixed step sizes and local optima in existing RRT-based methods.
- To enhance the search efficiency and reduce path length in complex environments for dual manipulator systems.
Main Methods:
- Introduced a probabilistic step size strategy incorporating target angle and random values.
- Integrated the artificial potential field method to guide the search.
- Employed a bidirectional search approach combined with goal bias and greedy path optimization.
- Utilized simulations to evaluate the performance of the proposed BPFPS-RRT algorithm.
Main Results:
- The BPFPS-RRT algorithm significantly reduced search time and path length for the main manipulator compared to existing methods (e.g., Goal Bias RRT, Variable Step Size RRT, Goal Bias Bidirectional RRT).
- Similar improvements in search time and path length were observed for the slave manipulator.
- Demonstrated a reduction in search time by up to 43.78% and path length by up to 21.38% for the main manipulator.
- Achieved reductions in search time by up to 46.88% and path length by up to 20.83% for the slave manipulator.
Conclusions:
- The proposed BPFPS-RRT algorithm effectively achieves path planning for dual manipulators.
- The novel step size strategy and integration of artificial potential fields enhance search efficiency and path optimality.
- BPFPS-RRT offers a robust solution for complex motion planning tasks involving dual robotic systems.
