Motion Planning for Robotics: A Review for Sampling-based Planners

Abstract

Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.

20 pages, 11 figures

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• Kinodynamic RRT*: Optimal Motion Planning for Systems with Linear Differential Constraints
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Cited by in corpus (5)

• Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
• Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization
• Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
• APT*: Asymptotically Optimal Motion Planning via Adaptively Prolated Elliptical R-Nearest Neighbors
• Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities