Range Limited Coverage Control using Air-Ground Multi-Robot Teams

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an apriori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd's algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to the Lloyd's algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd's algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.Comment: Published at 2021 IEEE International Conference on Robotics and Automation (ICRA

Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields

This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages the spatial structure of the coverage problem and presents a decentralized strategy where samples are aggregated locally by establishing communications through the boundaries of a Voronoi partition. We present an algorithm whereby each robot runs a local Gaussian process calculated from its own measurements and those provided by its Voronoi neighbors, which are incorporated into the individual robot's Gaussian process only if they provide sufficiently novel information. The performance of the algorithm is evaluated in simulation and compared with centralized approaches.Comment: Accepted IROS 202

Multi-robot collaboration functionalities for robot software development framework TeMoto

Robots enable us to operate in hazardous or otherwise unvisitable environments such as mines, fires and radioactive environments. TeMoto, which is built upon the Robotics Operating System (ROS), makes it easier to develop scalable, manageable and reliable software for robotics systems. TeMoto needs functionalities to represent the environment in order to support development of robot systems that are aware of their surroundings. The aim of this work is to plan and design a framework for working with environment models, enable robots to synchronize the environment data, create an environment model to match the framework and test the system in a real world scenario. This was achieved by implementing data structures representing objects and rooms/spaces in the, designing an abstract interface to work with the data structures and implementing the interface to create a corresponding environment model. The work resulted in a functional system and infrastructure, which allows sharing semantic and topological data between robots, which was demonstrated in a trash collecting mission featuring a heterogeneous multi-robot system. The implemented framework lays a foundation for use of environment models in TeMoto, which allows designing robot systems that interact with the world in a meaningful way.Eesti keeles: Robotid võimaldavad töötada eluohtlikes keskkondades või muul moel ligipääsmatutel aladel nagu näiteks kaevandustes, tulekahjude kustutamisel ja radioaktiivsetes keskkondades. Hõlbustamaks inimene-robot ja robot-robot koostöösüsteemide tarkvaraarendust on loodud robotite operatsioonisüsteemil (ROS) põhinev tarkvararaamistik TeMoto. TeMotol on vaja keskkonna esitamise funktsionaalsusi, et oleks võimalik arendada robotisüsteeme, mis on keskkonnast teadlikud. Töö eesmärgiks oli kavandada ja luua TeMoto arhitektuuris raamistik keskkonnamudelitega töötamiseks, luua funktsionaalsus keskkonnamudelite sünkroniseerimiseks TeMoto instantside vahel, pakkuda keskkonnamudel ja testida süsteemi reaalses stsenaariumis. Töö tulemusena valmis terviklik süsteem ja infrastruktuur, millega saab edukalt jagada semantilist ja topoloogilist informatsiooni mitme roboti vahel ja seda demonstreeriti heterogeense mitme roboti süsteemiga läbi viidud otsingumissiooni näitel. Implementeeritud TeMoto keskkonnamudeli raamistik paneb aluse keskkonnamudelite kasutusele TeMoto arhitektuuris, mis võimaldab TeMoto abil arendada keskkonnaga mõtestatult tegutsevaid robotsüsteeme

Control strategy for autonomous remediation of marine oil spills

Thesis (M.S.)--Boston UniversityThis thesis presents a novel formulation of a gradient-type controller to address the problem of cleaning up marine oil spills. Little work appears to have been done in developing autonomous oil spill clean-up devices, with most research efforts directed toward developing improved oil collection strategies. It does not appear that previous work in this field has included development of control algorithms specific to addressing the problem of deployment strategies for multiple clean-up devices. This thesis provides a framework for deployment of multiple clean-up agents and makes the following contributions to the field. We first develop a mathematical representation for the effect of a clean-up agent as a line-sink and introduce this term into an existing oil spill spreading model. The augmented oil spill spreading model is simulated for a finite volume of oil released within a region Q' which contains multiple clean-up agents. Second, we use the augmented oil spreading model to develop a cost function and derive a gradient controller that seeks to maximize the oil removal rate for a system of N clean-up agents. Several key properties of the controller are presented. Finally, we demonstrate the effectiveness of our controller through a MATLAB simulation. The performance of the controlled agents, measured by the total volume of oil removed over the simulation, is compared to the performance of static and randomly moving clean-up agents. The results from MATLAB simulations presented in this thesis demonstrate that the proposed control strategy is more effective at removing oil than static or randomly moving agents. The formulation of the control law directs clean-up devices toward areas in Q' experiencing the greatest volumetric change in oil, thereby maximizing the volume of oil that is removed by each agent. The controller presented in this thesis is adaptable to a range of clean-up devices and we present several future research avenues that could be pursued to further develop this concept

Distributed assembly strategies for teams of autonomous robots

The distributed assembly problem involves using a team of robots to assemble a structure autonomously. The goal is to develop a strategy such that the robots assemble the structure correctly and in the most e cient way possible. This thesis outlines di erent single robot assembly strategies, di erent methods for partitioning the building tasks amongst multiple robots, and de ning the complexity of a structure to be assembled. The scope of work includes investigating di erent assembly strategies through design and analysis of di erent assembly algorithms, developing simulations to evaluate and validate the di erent assembly strategies, comparing the proposed methods with existing approaches, and implementing selected assembly strategies on an actual robotic testbed.M.S., Mechanical Engineering -- Drexel University, 201