A Distributed Control Method Based on Neighbor Reward for Robot Swarm

Du, Lei; Huang, Yuhong; Tang, Yuhua; Huang, Da

doi:10.1145/3372047.3372085

Cited by 1 publication

(2 citation statements)

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“…Movements of UAVs are controlled by PID in this simulation, which makes the kinematics and dynamics of UAVs in touch with facts. We incorporated our neighbor reward modal with optimal reciprocal collision avoidance formulation into the existing obstacles simulation framework of [15]. All UAVs are randomly positioned at the lower left of the scenario.…”

Section: Resultsmentioning

confidence: 99%

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Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*

Tang

et al. 2022

J. Phys.: Conf. Ser.

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Navigating in an unknown area safely is counted as the underlying work which can support swarm agents for more complex tasks. When available information of search regions are lacking, agents make real-time action decisions according to surrounding environments they have perceived. For swarm agent system, connectivity maintenance and collision avoidance are both essential. Based on optimal Reciprocal Collision Avoidance (ORCA) algorithm, we proposed a method that agents can provide assistances to surrounding agents by spreading the status information of themselves, which is the neighbor reward method (NRM). This kind of status information contains ambient information and perceptions of the task which are transferred to reward data for convenient and uniform distributions. In other words, individuals utilize inter-neighbor interactions to achieve the same high-level goal, as well as result in an intelligent independent swarm agents system. This method solves the velocity selection problem of ORCA and optimizes the obstacle avoidance of the original NRM. The algorithm has been integrated in ROS framework and simulated on GAZEBO. In the tested scenario, our method is efficient for swarm agents collision avoidance in decentralized way.

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Section: Resultsmentioning

confidence: 99%

“…In our previous work [15], the obstacle avoidance strategy is wall following, based on artificial potential field. According to the distance from obstacles and goal of agent A, we get the repulsion/attractions which could deduce the velocity for agent A avoiding collisions [16][17][18].…”

Section: 1related Workmentioning

confidence: 99%

Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*

Tang

et al. 2022

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

A Distributed Control Method Based on Neighbor Reward for Robot Swarm

Cited by 1 publication

References 13 publications

Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*

Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*

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