Lynne E. Parker

ALLIANCE: an architecture for fault tolerant multirobot cooperation

ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robots own internal states. ALLIANCE is a fully distributed, behaviour-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.

Current State of the Art in Distributed Autonomous Mobile Robotics

As research progresses in distributed robotic systems, more and more aspects of multi-robot systems are being explored. This article surveys the current state of the art in distributed mobile robot systems. Our focus is principally on research that has been demonstrated in physical robot implementations. We have identified eight primary research topics within multi-robot systems — biological inspirations, communication, architectures, localization/mapping/exploration, object transport and manipulation, motion coordination, reconfigurable robots, and learning — and discuss the current state of research in these areas. As we describe each research area, we identify some key open issues in multi-robot team research. We conclude by identifying several additional open research issues in distributed mobile robotic systems.

Distributed Algorithms for Multi-Robot Observation of Multiple Moving Targets

An important issue that arises in the automation of many security, surveillance, and reconnaissance tasks is that of observing the movements of targets navigating in a bounded area of interest. A key research issue in these problems is that of sensor placement—determining where sensors should be located to maintain the targets in view. In complex applications involving limited-range sensors, the use of multiple sensors dynamically moving over time is required. In this paper, we investigate the use of a cooperative team of autonomous sensor-based robots for the observation of multiple moving targets. In other research, analytical techniques have been developed for solving this problem in complex geometrical environments. However, these previous approaches are very computationally expensive—at least exponential in the number of robots—and cannot be implemented on robots operating in real-time. Thus, this paper reports on our studies of a simpler problem involving uncluttered environments—those with either no obstacles or with randomly distributed simple convex obstacles. We focus primarily on developing the on-line distributed control strategies that allow the robot team to attempt to minimize the total time in which targets escape observation by some robot team member in the area of interest. This paper first formalizes the problem (which we term CMOMMT for Cooperative Multi-Robot Observation of Multiple Moving Targets) and discusses related work. We then present a distributed heuristic approach (which we call A-CMOMMT) for solving the CMOMMT problem that uses weighted local force vector control. We analyze the effectiveness of the resulting weighted force vector approach by comparing it to three other approaches. We present the results of our experiments in both simulation and on physical robots that demonstrate the superiority of the A-CMOMMT approach for situations in which the ratio of targets to robots is greater than 1/2. Finally, we conclude by proposing that the CMOMMT problem makes an excellent domain for studying multi-robot learning in inherently cooperative tasks. This approach is the first of its kind for solving the on-line cooperative observation problem and implementing it on a physical robot team.

A distributed and optimal motion planning approach for multiple mobile robots

We propose a distributed and optimal motion planning algorithm for multiple robots. The computationally expensive problem is decomposed into two modules: path planning and velocity planning. The D* search method is applied in both modules, based on either geometric formulation or schedule formulation. Optimization is achieved at the individual robot level by defining cost functions, and also at the team level by a global measurement function reflecting performance indices of interest as a team. Contrary to our knowledge of previous results on multi-robot motion planning that either obtain optimal solutions through centralized and exhaustive computing, or achieve distributed implementations without considering any optimization issues, our approach combines these two features and explicitly optimizes performance functions through a distributed implementation. It is also one of the few that is capable of handling outdoor rough terrain environments and real time replanning. Simulations are shown on a Mars-like rough terrain using a 3D vehicle planner and control simulator. The algorithm was also implemented and successfully run on a group of Nomad 200 indoor robots.

Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection

We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment.

ALLIANCE: an architecture for fault tolerant, cooperative control of heterogeneous mobile robots

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. We describe a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. The software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, we describe in detail our experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. >

Multi-Robot Systems: From Swarms to Intelligent Automata

Preface. Part I: Planning and Task Allocation. On Architectural and Decisional Issues for Multi-Robot Cooperation R. Alami. A Framework for Studying Multi-Robot Task Allocation B.P. Gerkey, M.J. Matarie. Market-Based Multi-Robot Planning in a Distributed Layered Architecture D. Goldberg, V. Cicirello, M.B. Dias Reid Simmons, S. Smith, A. Stentz. Collaborative Tasking of Tightly Constrained Multi-Robot Missions D.C. MacKenzie. Part II: Coordination and Control. Multi-Objective Navigation and Control Using Interval Prograniming M.R. Benjamin. Cooperative Relative Localization for Mobile Robot Teams: An Ego-Centric Approach A. Howard, M.J. Mataric, G.S. Sukhatine. Fractional Bandwidth Reacquisition Algorithms for VSW-MCM B. Cook, D. Marthaler, C. Topaz, A. Bertozzi, M. Kemp. Dynamic Multi-Robot Coordination D. Vail, M. Veloso. Part III: Sensor and Information Fusion. Information Sharing in Teams of Self-Aware Entities J. Franke, B. Satterfield, S. Jameson. Realizing Virtual Sensors by Distributed Multi-Level Sensor Fusion E. Nett, S. Schemmer. Information Fusion and Control for Multiple UAVs S. Sukkarieh, E. Nettleton, B. Grocholsky, H. Durrant-Whyte. Part IV: Learning. Multistrategy Learning Methods for Multirobot Systems R.C. Arkin, Y. Endo, B. Lee, D. MacKenzie, E. Martinson. Part V: Self-Reconfigurable Robots. Distributed Locomotion Algorithms for Self-Reconfigurable Robots Operating on Rough Terrain Z. Butler, D. Rus. Self-Assembly in Space via Self-Reconfigurable Robots Wei-Min Shen, P. Will, B. Khoshnevis. Part VI: Large-Scale Robot Teams. Optimization of Swarm Robotic Systems via Macroscopic Models A. Martinoli, K. Easton. CentiBOTS: Large Scale Robot Teams K. Konolige, C. Ortiz, R. Vincent, A. Agno, M. Eriksen, B. Limketkai, M. Lewis, L. Briesemeister, E. Ruspini, D. Fox, J. Ko, B. Stewart, L. Guibas. The Effect of Heterogeneity in Teams of 100+ Mobile Robots L.E. Parker. Part VII: Human-Robot Teams. Collaborative Tools for Mixed Teams of Humans and Robots D.J. Bruemmer, M.C. Walton. Mixed-Initiative Control of Large Human-Robot Teams C.L. Johnson. Experiments in Human-Robot Teams C.W. Nielsen, M.A. Goodrich, J.W. Crandall. Impact of Autonomy in Multirobot Systems on Teleoperation Performance B. Trouvain, H.L. Wolf, F.E. Schneider. Part VIII: Communication Constraint and Networks. Decentralized Motion Planning for Multiple Robots Subject to Sensing and Communication Constraints G.A.S. Pereira, A.K. Das, V. Kumar, M.F.M. Campos. Exploiting the Interactions Between Robotic Autonomy and Networks J. Redi, J. Bers. Part IX: Poster Abstracts. Observing Multiple Targets with Multiple Mobile Robots Using Probabilistic Behavior-Based Techniques T. Balch, A. Stroupe. Dynamic Coverage via Multi-robot Cooperation M.A. Batalin, G.S. Sukhatme. Emergent Control Surveillance Networks R.R. Brooks, J. Lamb, M. Pirretti, M. Zhu, S.S. Iyengar. On Dynamic Reconfiguration of Multi-Robot Formations R. Fierro, A.K. Das. Distributed Multi-Robot Mapping D. Fox, J. Ko, B. Stewart, K. Konolige, B. Limetkai. An Endogeneous Architecture for Cooperative Sensor Teams B. Grocholsky, A. Makarenko, H. Durrant-Whyte. Efficient Inference Grounding for Distributed Multi-Robot Teams A. Khoo, I.D. Horswill. Author Index.

Robot Teams: From Diversity to Polymorphism

This is a comprehensive volume on robot teams that will be the standard reference on multi-robot systems. The volume provides not only the essentials of multi-agent robotics theory but also descriptions of exemplary implemented systems demonstrating the key concepts of multi-robot research. Information is presented in a descriptive manner and augmented with detailed mathematical formulations, photos, diagrams, and source code examples.

Multiple Mobile Robot Systems

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Cooperative Robotics for Multi-Target Observation

An important issue that arises in the automation of many security, surveillance, and reconnaissance tasks is that of observing (or monitoring) the movements of targets navigating in a bounded area ...