深度を考慮したノイズモデルによるマルチエージェント能動探索
Multi-Agent Active Search using Realistic Depth-Aware Noise Model
距離や遮蔽による物体検出の不確実性を考慮し、Thompson Samplingで分散協調する複数ロボットの能動探索アルゴリズムNATSを提案し、シミュレーションで既存手法を上回ることを示した。
著者: Ramina Ghods, William J. Durkin, Jeff Schneider
分類: cs.RO, cs.AI, cs.LG, eess.SP
原文アブストラクト
The active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers. Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored. Additionally, in many active search scenarios, communication infrastructure may be unreliable or unestablished, making centralized control of multiple agents impractical. We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robots performing active search considering two sources of sensory information from monocular optical imagery and depth maps. By utilizing Thompson Sampling, NATS allows for decentralized coordination among multiple agents. NATS also considers object detection uncertainty from depth as well as environmental occlusions and operates while remaining agnostic of the number of objects of interest. Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search. We demonstrate the real-world viability of NATS using a pseudo-realistic environment created in the Unreal Engine 4 game development platform with the AirSim plugin.
