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Multi-Agent Active Search using Realistic Depth-Aware Noise Model | | Ramina Ghods
; William J. Durkin
; Jeff Schneider
; | | Date: |
10 Nov 2020 | | Abstract: | The search for objects of interest in an unknown environment by making
data-collection decisions (i.e., active search or active sensing) has robotics
applications in many fields, including the search and rescue of human survivors
following disasters, detecting gas leaks or locating and preventing 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.
An additional challenge is that in many active search scenarios, communication
infrastructure may be damaged, unreliable, or unestablished, making centralized
control of multiple search agents impractical. We present an algorithm called
Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple
ground-based robot agents performing active search considering two sources of
sensory information from monocular optical imagery and sonar tracking. NATS
utilizes communications between robot agents in a decentralized manner that is
robust to intermittent loss of communication links. Additionally, it takes into
account object detection uncertainty from depth as well as environmental
occlusions. 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 photo-realistic
environment created in the Unreal Engine 4 game development platform with the
AirSim plugin. | | Source: | arXiv, 2011.04825 | | Services: | Forum | Review | PDF | Favorites | |
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