An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots
This paper addresses the challenge of collision avoidance in multi-agent autonomous robotic systems. To design the control input in a distributed manner, traditional methods using Control Barrier Functions (CBFs) for collision avoidance propose allocation rules of the constraints, which are about th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2025.2457203 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849338193139204096 |
|---|---|
| author | Hayato Dan Hiroki Etchu Kanon Yokoi Yuki Origane Daisuke Kurabayashi |
| author_facet | Hayato Dan Hiroki Etchu Kanon Yokoi Yuki Origane Daisuke Kurabayashi |
| author_sort | Hayato Dan |
| collection | DOAJ |
| description | This paper addresses the challenge of collision avoidance in multi-agent autonomous robotic systems. To design the control input in a distributed manner, traditional methods using Control Barrier Functions (CBFs) for collision avoidance propose allocation rules of the constraints, which are about the control inputs and are derived from CBFs to avoid the collision. However, these methods sometimes suffer from the infeasibility of Quadratic-programming (QP)-based controllers and noise due to the need for relative velocity information. To overcome these limitations, we first derive the necessary conditions for the allocation rule that decouples constraints derived from CBFs to guarantee the collision avoidance. We then propose a novel allocation rule that asymmetrically decouples the constraints, that satisfies the derived conditions. This allocation rule can enhance the feasibility of controllers and eliminate the need for relative velocity information. Finally, we demonstrate its effectiveness through simulations and real-world experiments. The results show that the proposed method successfully ensures collision avoidance, maintaining safety distances without relying on relative velocity data. Our contributions include: (1) defining the conditions for the allocation rule, (2) proposing a constraint allocation rule excluding relative velocity, (3) validating the feasibility of controllers through simulations, and (4) demonstrating real-world applicability with experiments, thus offering a practical solution for autonomous robotic systems. |
| format | Article |
| id | doaj-art-5e16a6f3c98a49b58a062b2a591c9439 |
| institution | Kabale University |
| issn | 1884-9970 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj-art-5e16a6f3c98a49b58a062b2a591c94392025-08-20T03:44:28ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702025-12-0118110.1080/18824889.2025.24572032457203An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robotsHayato Dan0Hiroki Etchu1Kanon Yokoi2Yuki Origane3Daisuke Kurabayashi4Institute of Science TokyoInstitute of Science TokyoInstitute of Science TokyoInstitute of Science TokyoInstitute of Science TokyoThis paper addresses the challenge of collision avoidance in multi-agent autonomous robotic systems. To design the control input in a distributed manner, traditional methods using Control Barrier Functions (CBFs) for collision avoidance propose allocation rules of the constraints, which are about the control inputs and are derived from CBFs to avoid the collision. However, these methods sometimes suffer from the infeasibility of Quadratic-programming (QP)-based controllers and noise due to the need for relative velocity information. To overcome these limitations, we first derive the necessary conditions for the allocation rule that decouples constraints derived from CBFs to guarantee the collision avoidance. We then propose a novel allocation rule that asymmetrically decouples the constraints, that satisfies the derived conditions. This allocation rule can enhance the feasibility of controllers and eliminate the need for relative velocity information. Finally, we demonstrate its effectiveness through simulations and real-world experiments. The results show that the proposed method successfully ensures collision avoidance, maintaining safety distances without relying on relative velocity data. Our contributions include: (1) defining the conditions for the allocation rule, (2) proposing a constraint allocation rule excluding relative velocity, (3) validating the feasibility of controllers through simulations, and (4) demonstrating real-world applicability with experiments, thus offering a practical solution for autonomous robotic systems.http://dx.doi.org/10.1080/18824889.2025.2457203multi-agent systemsautonomous robotcollision avoidancecontrol barrier functionsconstraint-based control |
| spellingShingle | Hayato Dan Hiroki Etchu Kanon Yokoi Yuki Origane Daisuke Kurabayashi An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots SICE Journal of Control, Measurement, and System Integration multi-agent systems autonomous robot collision avoidance control barrier functions constraint-based control |
| title | An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| title_full | An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| title_fullStr | An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| title_full_unstemmed | An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| title_short | An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| title_sort | asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots |
| topic | multi-agent systems autonomous robot collision avoidance control barrier functions constraint-based control |
| url | http://dx.doi.org/10.1080/18824889.2025.2457203 |
| work_keys_str_mv | AT hayatodan anasymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT hirokietchu anasymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT kanonyokoi anasymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT yukiorigane anasymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT daisukekurabayashi anasymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT hayatodan asymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT hirokietchu asymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT kanonyokoi asymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT yukiorigane asymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots AT daisukekurabayashi asymmetricallocationruleofconstraintsderivedfromcontrolbarrierfunctionforcollisionavoidancebetweenautonomousrobots |