Academic Journal
Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking.
| Τίτλος: | Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking. |
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| Συγγραφείς: | Umbarkar, Ashish, Patle, Bhumeshwar K., Sanap, Sudarshan, Patel, Brijesh |
| Πηγή: | Machines; Aug2026, Vol. 14 Issue 8, p870, 73p |
| Θεματικοί όροι: | Robotic path planning, Mobile robots, Deep learning, Multiagent systems, Benchmark problems (Computer science), Metaheuristic algorithms, Benchmark testing (Engineering), Algorithms |
| Περίληψη: | This survey presents a large-scale, reproducible, and parameter-mapped benchmarking analysis of path planning algorithms for multiple mobile robot systems (MMRS) by systematically examining 247 rigorously filtered papers from high-impact journals. Unlike prior reviews that primarily provide conceptual taxonomies, this survey introduces execution-oriented multi-parameter mapping enabling direct comparison of classical planners (A*, D*, Cell Decomposition, APF, RM, RRT, and ORCA), nature-inspired metaheuristics (PSO, GA, ACO, GWO, FA, ABC, BFO, CS, BA, SFLA, eagle-inspired optimizers), and learning-driven AI frameworks (Fuzzy Logic, Artificial Neural Networks, and Deep Reinforcement Learning). Each paper is evaluated across 15 practical planning dimensions, including environment type (static 95% vs. dynamic 51%), multi-robot validation (52%), dynamic goal handling (13%), energy awareness (14%), timepath optimization bias (82% focus), inter-robot coordination (less than 47%), and software validation platforms (MATLAB 42% and ROS 9%), revealing that simulation-only validation dominates (98%) while experimental testing remains limited (33%). Multivariate validation through Multiple Correspondence Analysis further confirms that coordination maturity, energy awareness, and multi-robot applicability are the primary structural differentiators of deployment readiness across algorithm families. The findings emphasize the need for hybrid, energy-aware, and coordination-driven MRPP frameworks supported by experimental benchmarking and reproducible deployment pipelines to advance real-world MMRS autonomy. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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