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| Natura: | Recurso digital |
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Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.17472140 |
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| _version_ | 1866901537929822208 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Golf carts exhibit unique dynamic behavior and motion patterns on a golf course. Their operation embodies target drive, slope adaptation, energy balance, obstacle avoidance coordination, and inertial coordination. Inspired by the driving mechanism of golf carts, this paper proposes a dynamic drive-path coordination optimization (DDPC) algorithm. This algorithm simulates the process of a golf cart automatically adjusting its speed, direction, and energy consumption in complex terrain. It constructs a multi-factor position update mechanism that integrates target attraction, slope energy regulation, obstacle avoidance, and inertial coordination. This paper deduces the algorithm's mathematical formula in detail, demonstrates its convergence and stability characteristics in the search space, and theoretically demonstrates its applicability and superiority in complex optimization problems. The results demonstrate the algorithm's strong adaptability, high global search capability, and path smoothness, providing a novel biomimetic-inspired approach for the design of intelligent optimization algorithms.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17472140 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dynamic Drive-Path Coordination Optimization Algorithm Zhang, Jincheng <p><span>Golf carts exhibit unique dynamic behavior and motion patterns on a golf course. Their operation embodies target drive, slope adaptation, energy balance, obstacle avoidance coordination, and inertial coordination. Inspired by the driving mechanism of golf carts, this paper proposes a dynamic drive-path coordination optimization (DDPC) algorithm. This algorithm simulates the process of a golf cart automatically adjusting its speed, direction, and energy consumption in complex terrain. It constructs a multi-factor position update mechanism that integrates target attraction, slope energy regulation, obstacle avoidance, and inertial coordination. This paper deduces the algorithm's mathematical formula in detail, demonstrates its convergence and stability characteristics in the search space, and theoretically demonstrates its applicability and superiority in complex optimization problems. The results demonstrate the algorithm's strong adaptability, high global search capability, and path smoothness, providing a novel biomimetic-inspired approach for the design of intelligent optimization algorithms.</span></p> |
| title | Dynamic Drive-Path Coordination Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.17472140 |