scholarly journals Asymptotically Optimal Planning for Non-Myopic Multi-Robot Information Gathering

2019 ◽  
Author(s):  
Yiannis Kantaros ◽  
Brent Schlotfeldt ◽  
Nikolay Atanasov ◽  
George J. Pappas
Keyword(s):  
Information Gathering ◽  
Optimal Planning ◽  
Asymptotically Optimal ◽  
Multi Robot

scholarly journals DeepIG: Multi-Robot Information Gathering With Deep Reinforcement Learning

2019 ◽  
Vol 4 (3) ◽  
pp. 3059-3066 ◽  
Author(s):  
Alberto Viseras ◽  
Ricardo Garcia
Keyword(s):  
Reinforcement Learning ◽  
Information Gathering ◽  
Multi Robot

scholarly journals Multi-robot Information Gathering for Precision Agriculture: Current State, Scope, and Challenges

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ayan Dutta ◽  
Swapnoneel Roy ◽  
O. Patrick Kreidl ◽  
Ladislau Boloni
Keyword(s):  
Precision Agriculture ◽  
Information Gathering ◽  
Current State ◽  
Multi Robot

scholarly journals dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning

2019 ◽  
Vol 44 (3-4) ◽  
pp. 443-467 ◽  
Author(s):  
Rahul Shome ◽  
Kiril Solovey ◽  
Andrew Dobson ◽  
Dan Halperin ◽  
Kostas E. Bekris
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Asymptotically Optimal ◽  
Multi Robot

Sampling-based planning for non-myopic multi-robot information gathering

2021 ◽  
Author(s):  
Yiannis Kantaros ◽  
Brent Schlotfeldt ◽  
Nikolay Atanasov ◽  
George J. Pappas
Keyword(s):  
Information Gathering ◽  
Multi Robot

scholarly journals Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 484
Author(s):  
Alberto Viseras ◽  
Zhe Xu ◽  
Luis Merino
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Physical Process ◽  
Wind Field ◽  
Complex Dynamics ◽  
Information Gathering ◽  
Distributed Decision Making ◽  
Novel Approach ◽  
Distributed Decision ◽  
Multi Robot

Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach.

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