An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario
This paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities de...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/6/474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839653507260481536 |
|---|---|
| author | Jingrui Zhang Zhenhong Xu Houde Liu Xiaojun Zhu Bin Lan |
| author_facet | Jingrui Zhang Zhenhong Xu Houde Liu Xiaojun Zhu Bin Lan |
| author_sort | Jingrui Zhang |
| collection | DOAJ |
| description | This paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities derived from prior knowledge and expert estimations, a mathematical model for multi-map path planning in mine disaster rescue scenarios is developed. An improved hybrid algorithm combining ant colony optimization (ACO) and genetic algorithm (GA) is then proposed to solve the established model. In the hybrid approach, the improved ACO is employed to overcome the limitations of traditional genetic algorithms, such as poor initial population quality, slow convergence, and suboptimal results. Additionally, a grid-based, rectangular-area, obstacle avoidance strategy is incorporated to precisely evaluate the obstacle avoidance path of each individual across different obstacle maps. Finally, the feasibility and effectiveness of the proposed hybrid algorithm are validated through simulations involving both single and multiple mine disaster maps. The results demonstrate the potential of the proposed approach for solving robot path optimization problems in complex multi-environment scenarios. |
| format | Article |
| id | doaj-art-f18bc5fce6d2434babb449a2055f9ae6 |
| institution | Matheson Library |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-f18bc5fce6d2434babb449a2055f9ae62025-06-25T14:07:07ZengMDPI AGMachines2075-17022025-05-0113647410.3390/machines13060474An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster ScenarioJingrui Zhang0Zhenhong Xu1Houde Liu2Xiaojun Zhu3Bin Lan4Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaTsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaJianghuai Advance Technology Center, Hefei 230001, ChinaJianghuai Advance Technology Center, Hefei 230001, ChinaThis paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities derived from prior knowledge and expert estimations, a mathematical model for multi-map path planning in mine disaster rescue scenarios is developed. An improved hybrid algorithm combining ant colony optimization (ACO) and genetic algorithm (GA) is then proposed to solve the established model. In the hybrid approach, the improved ACO is employed to overcome the limitations of traditional genetic algorithms, such as poor initial population quality, slow convergence, and suboptimal results. Additionally, a grid-based, rectangular-area, obstacle avoidance strategy is incorporated to precisely evaluate the obstacle avoidance path of each individual across different obstacle maps. Finally, the feasibility and effectiveness of the proposed hybrid algorithm are validated through simulations involving both single and multiple mine disaster maps. The results demonstrate the potential of the proposed approach for solving robot path optimization problems in complex multi-environment scenarios.https://www.mdpi.com/2075-1702/13/6/474rescue robot path planningmulti-environment map rescuingimproved ant colony–genetic hybrid algorithmrectangular obstacle avoidance strategy |
| spellingShingle | Jingrui Zhang Zhenhong Xu Houde Liu Xiaojun Zhu Bin Lan An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario Machines rescue robot path planning multi-environment map rescuing improved ant colony–genetic hybrid algorithm rectangular obstacle avoidance strategy |
| title | An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario |
| title_full | An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario |
| title_fullStr | An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario |
| title_full_unstemmed | An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario |
| title_short | An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario |
| title_sort | improved hybrid ant colony optimization and genetic algorithm for multi map path planning of rescuing robots in mine disaster scenario |
| topic | rescue robot path planning multi-environment map rescuing improved ant colony–genetic hybrid algorithm rectangular obstacle avoidance strategy |
| url | https://www.mdpi.com/2075-1702/13/6/474 |
| work_keys_str_mv | AT jingruizhang animprovedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT zhenhongxu animprovedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT houdeliu animprovedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT xiaojunzhu animprovedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT binlan animprovedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT jingruizhang improvedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT zhenhongxu improvedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT houdeliu improvedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT xiaojunzhu improvedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario AT binlan improvedhybridantcolonyoptimizationandgeneticalgorithmformultimappathplanningofrescuingrobotsinminedisasterscenario |