Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints
Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8733044/ |
| _version_ | 1818924183289069568 |
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| author | Jian Yang Xin Wang Peter Bauer |
| author_facet | Jian Yang Xin Wang Peter Bauer |
| author_sort | Jian Yang |
| collection | DOAJ |
| description | Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments. |
| first_indexed | 2024-12-20T02:21:17Z |
| format | Article |
| id | doaj.art-b093187014784ed488d9b11b22f866aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:21:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b093187014784ed488d9b11b22f866aa2022-12-21T19:56:49ZengIEEEIEEE Access2169-35362019-01-017763287634110.1109/ACCESS.2019.29216218733044Extended PSO Based Collaborative Searching for Robotic Swarms With Practical ConstraintsJian Yang0https://orcid.org/0000-0003-0488-1514Xin Wang1Peter Bauer2Department of Mechanical and Automation Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, ChinaDepartment of Mechanical and Automation Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, ChinaDepartment of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USAApplying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.https://ieeexplore.ieee.org/document/8733044/Swarm roboticsparticle swarm optimization (PSO)collaborative searchingcollision avoidance |
| spellingShingle | Jian Yang Xin Wang Peter Bauer Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints IEEE Access Swarm robotics particle swarm optimization (PSO) collaborative searching collision avoidance |
| title | Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints |
| title_full | Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints |
| title_fullStr | Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints |
| title_full_unstemmed | Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints |
| title_short | Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints |
| title_sort | extended pso based collaborative searching for robotic swarms with practical constraints |
| topic | Swarm robotics particle swarm optimization (PSO) collaborative searching collision avoidance |
| url | https://ieeexplore.ieee.org/document/8733044/ |
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