Centroidal area-constrained partitioning for robotic networks

We consider the problem of optimal coverage with area constraints in a mobile multi-agent system. For a planar environment with an associated density function, this problem is equivalent to dividing the environment into optimal subregions such that each agent is responsible for the coverage of its own region. In this paper, we design a continuous-time distributed policy which allows a team of agents to achieve a convex area-constrained partition of a convex workspace. Our work is related to the classic Lloyd algorithm, and makes use of generalized Voronoi diagrams. We also discuss practical implementation for real mobile networks. Simulation methods are presented and discussed

MARLAS: Multi Agent Reinforcement Learning for cooperated Adaptive Sampling

The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the robots. In this paper, we propose a robust and scalable approach using decentralized Multi-Agent Reinforcement Learning for cooperated Adaptive Sampling (MARLAS) of quasi-static environmental processes. Given a prior on the field being sampled, the proposed method learns decentralized policies for a team of robots to sample high-utility regions within a fixed budget. The multi-robot adaptive sampling problem requires the robots to coordinate with each other to avoid overlapping sampling trajectories. Therefore, we encode the estimates of neighbor positions and intermittent communication between robots into the learning process. We evaluated MARLAS over multiple performance metrics and found it to outperform other baseline multi-robot sampling techniques. We further demonstrate robustness to communication failures and scalability with both the size of the robot team and the size of the region being sampled. The experimental evaluations are conducted both in simulations on real data and in real robot experiments on demo environmental setup

Multi-Robot workspace division based on compact polygon decomposition

In this work, we tackle the problem of multi-robot convex workspace division. We present an algorithm to split a convex area among several robots into the corresponding number of parts based on the area requirements for each part. The core idea of the algorithm is a sequence of divisions into pairs with the lowest possible perimeters. In this way, the compactness of the partitions obtained is maximized. The performance of the algorithm, as well as the quality of the obtained parts, are analyzed in comparison with two different algorithms. The presented approach yields better results in all metrics compared to other algorithms.This work was supported by the Ministerio de Economía, Industria y Competitividad, and Gobierno de España under Award BES-2017-079798 and Award TRA2016-77012-R.Peer ReviewedPostprint (published version

RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System

Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.Comment: Conditionally accpeted by TR

Implementation of distributed partitioning algorithms using mobile Wheelphones

This thesis presents the implementation process of partitioning algorithms from the theorical ideas to sperimental result