A Comprehensive Review of Path-Planning Algorithms for Planetary Rover Exploration
Open accessAbstract
Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, illumination conditions, and temperature fluctuations, necessitating advanced path-planning strategies to ensure mission success. This review comprehensively synthesizes recent advancements in planetary rover path-planning algorithms. First, we categorize these algorithms from a constraint-oriented perspective, distinguishing between internal rover state constraints and external environmental constraints. Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. Subsequently, we explore bio-inspired path-planning methods, such as evolutionary algorithms, fuzzy computing, and machine learning-based approaches, with a particular emphasis on the latest developments and prospects of machine learning techniques in planetary rover navigation. Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions.
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Most works are limited to single or few regions of interest, visual semantic or grasping tasks, or other robotic platforms, but not specifically for benchmarking rover global path planning.
“most works are limited to single or few regions of interest (Tamar et al., 2016; Miao and Wei, 2025), visual semantic or grasping tasks (Shuaifeng et al., 2025; Orsula et al., 2022), or other robotic platforms (Richter et al., 2024), but not specifically for benchmarking rover global path planning.”
From Planetary Terrain Datasets and Benchmarks for Rover Path Planning (2025) · cites this paper as Miao and Wei, 2025
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- Authors
- Miao, Q., & Wei, G.
- Affiliations
- Deep Space Exploration Laboratory (CN)
- Corresponding author
- Wei, Guangfei
- ORCID
- Wei, Guangfei
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2025-05-31
- Volume
- 17
- Issue
- 11
- Pages
- 1924
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4410966838
- Cited by
- 13
- References
- 107
- Citations by year
- 2026: 5 · 2025: 8
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Anhui Natural Science Foundation (2408085Y021, 42473053)
- National Natural Science Foundation of China (2408085Y021, 42473053)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- planetary rovers, path planning, autonomous navigation, potential field method, sampling-based path planning, dynamic window approach, graph search, bio-inspired algorithms, evolutionary algorithms, fuzzy computing, environmental constraints
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