Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization
This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/13/3/46 |
| _version_ | 1797239356005548032 |
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| author | Mingeuk Kim Minyoung Lee Byeongjin Kim Moohyun Cha |
| author_facet | Mingeuk Kim Minyoung Lee Byeongjin Kim Moohyun Cha |
| author_sort | Mingeuk Kim |
| collection | DOAJ |
| description | This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world. |
| first_indexed | 2024-04-24T17:50:14Z |
| format | Article |
| id | doaj.art-1cc1dd28af804327b6c361859f6cb848 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-04-24T17:50:14Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-1cc1dd28af804327b6c361859f6cb8482024-03-27T14:03:10ZengMDPI AGRobotics2218-65812024-03-011334610.3390/robotics13030046Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm OptimizationMingeuk Kim0Minyoung Lee1Byeongjin Kim2Moohyun Cha3Korea Institute of Machinery and Materials, Daejeon 34103, Republic of KoreaKorea Institute of Machinery and Materials, Daejeon 34103, Republic of KoreaKorea Institute of Machinery and Materials, Daejeon 34103, Republic of KoreaKorea Institute of Machinery and Materials, Daejeon 34103, Republic of KoreaThis paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world.https://www.mdpi.com/2218-6581/13/3/46autonomous ground vehiclepath planningmodel predictive controlparticle swarm optimizationpotential fielddriving simulation |
| spellingShingle | Mingeuk Kim Minyoung Lee Byeongjin Kim Moohyun Cha Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization Robotics autonomous ground vehicle path planning model predictive control particle swarm optimization potential field driving simulation |
| title | Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization |
| title_full | Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization |
| title_fullStr | Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization |
| title_full_unstemmed | Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization |
| title_short | Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization |
| title_sort | development of local path planning using selective model predictive control potential fields and particle swarm optimization |
| topic | autonomous ground vehicle path planning model predictive control particle swarm optimization potential field driving simulation |
| url | https://www.mdpi.com/2218-6581/13/3/46 |
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