An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications
Global path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative s...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/5/1326 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850222735756623872 |
|---|---|
| author | Peng Li Lei Wei Dongsu Wu |
| author_facet | Peng Li Lei Wei Dongsu Wu |
| author_sort | Peng Li |
| collection | DOAJ |
| description | Global path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative strategies. First, the early search efficiency is improved by implementing a non-uniform initial pheromone distribution. Second, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-greedy strategy is employed to adjust the state transition probability, thereby balancing exploration and exploitation. Third, adaptive dynamic adjustment of the exponents <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> is realized, dynamically balancing the pheromone and heuristic function. Fourth, a multi-objective heuristic function considering both target distance and turning angle is constructed to enhance the quality of node selection. Fifth, a dynamic global pheromone update strategy is designed to prevent the algorithm from prematurely converging to local optima. Finally, by introducing multi-objective performance indicators, the path planning problem is transformed into a multi-objective optimization problem, enabling more comprehensive path optimization. Systematic simulations and experimentation were performed to validate the effectiveness of IEACO. The simulation results confirm the efficacy of each improvement in IEACO and demonstrate its performance advantages over other algorithms. The experimental results further highlight the practical value of IEACO in solving global path planning problems for mobile robots. |
| format | Article |
| id | doaj-art-1aca0dc1e0544e6bbb45fd209b720c38 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1aca0dc1e0544e6bbb45fd209b720c382025-08-20T02:06:15ZengMDPI AGSensors1424-82202025-02-01255132610.3390/s25051326An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering ApplicationsPeng Li0Lei Wei1Dongsu Wu2College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210026, ChinaGlobal path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative strategies. First, the early search efficiency is improved by implementing a non-uniform initial pheromone distribution. Second, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-greedy strategy is employed to adjust the state transition probability, thereby balancing exploration and exploitation. Third, adaptive dynamic adjustment of the exponents <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> is realized, dynamically balancing the pheromone and heuristic function. Fourth, a multi-objective heuristic function considering both target distance and turning angle is constructed to enhance the quality of node selection. Fifth, a dynamic global pheromone update strategy is designed to prevent the algorithm from prematurely converging to local optima. Finally, by introducing multi-objective performance indicators, the path planning problem is transformed into a multi-objective optimization problem, enabling more comprehensive path optimization. Systematic simulations and experimentation were performed to validate the effectiveness of IEACO. The simulation results confirm the efficacy of each improvement in IEACO and demonstrate its performance advantages over other algorithms. The experimental results further highlight the practical value of IEACO in solving global path planning problems for mobile robots.https://www.mdpi.com/1424-8220/25/5/1326mobile robotsant colony optimization algorithmglobal path planningstate transition probability |
| spellingShingle | Peng Li Lei Wei Dongsu Wu An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications Sensors mobile robots ant colony optimization algorithm global path planning state transition probability |
| title | An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications |
| title_full | An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications |
| title_fullStr | An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications |
| title_full_unstemmed | An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications |
| title_short | An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications |
| title_sort | intelligently enhanced ant colony optimization algorithm for global path planning of mobile robots in engineering applications |
| topic | mobile robots ant colony optimization algorithm global path planning state transition probability |
| url | https://www.mdpi.com/1424-8220/25/5/1326 |
| work_keys_str_mv | AT pengli anintelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications AT leiwei anintelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications AT dongsuwu anintelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications AT pengli intelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications AT leiwei intelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications AT dongsuwu intelligentlyenhancedantcolonyoptimizationalgorithmforglobalpathplanningofmobilerobotsinengineeringapplications |