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Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance
Takeshi Kano1, Takeru Kanno1,2, Taishi Mikami1,2
1Research Institute of Electrical Communication, Tohoku University, Sendai, Japan.
Frontiers in Robotics and AI
|November 28, 2022
Summary
This study introduces active sensing for multi-agent systems, enabling robots and drones to move safely and efficiently. By controlling observation ranges based on collision risk, it reduces unnecessary sensing and computation.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Theory
Background:
- Multi-agent systems require efficient collision avoidance for tasks like warehouse logistics and drone delivery.
- Existing control schemes struggle to balance speed, safety, and minimal acceleration/deceleration.
Purpose of the Study:
- To develop a decentralized control scheme for multi-agent systems that enhances efficiency and safety.
- To address the issue of unnecessary sensing and computation in collision avoidance.
Main Methods:
- Modified the social force model, a pedestrian dynamics model, for agent control.
- Introduced active sensing, dynamically adjusting agent observation ranges based on a defined 'collision risk level'.
Main Results:
- Achieved quick, smooth, and safe movement in multi-agent simulations.
- Demonstrated significant reduction in unnecessary sensing and calculations per agent.
Conclusions:
- The proposed active sensing control scheme effectively manages multi-agent navigation.
- This approach optimizes resource utilization by reducing computational load and sensor data requirements.
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