ALLIANCE is a software architecture that fa- cilitates 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 robot's own internal states. ALLIANCE is a fully distributed, behavior-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.

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of collective robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations. 1

Abstract — In this paper the problem of dynamic selfreconfiguration of a class of modular robotic systems referred to as metamorphic systems is examined. A metamorphic robotic system is a collection of mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules. We examine the near-optimal reconfiguration of a metamorphic robot from an arbitrary initial configuration to a desired final configuration. Concepts of distance between metamorphic robot configurations are defined, and shown to satisfy the formal properties of a metric. These metrics, called configuration metrics, are then applied to the automatic self-reconfiguration of metamorphic systems in the case when one module is allowed to move at a time. There is no simple method for computing the optimal sequence of moves required to reconfigure. As a result, heuristics which can give a near optimal solution must be used. We use the technique of Simulated Annealing to drive the reconfiguration process with configuration metrics as cost functions. The relative performance of simulated annealing with different cost functions is compared and the usefulness of the metrics developed in this paper is demonstrated. Index Terms—Metrics, optimal assignment, self-reconfigurable robots, simulated annealing.

This paper discusses issues in the design and implementation of metamorphic robotic systems. A metamorphic robotic system is a collection of independently controlled mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules. A metamorphic system can dynamically reconfigure by the locomotion of modules over their neighbors. Thus they can be viewed as a collection of connected modular robots which act together to perform the given task. The planar metamorphic robots described in this paper consist of hexagonal or square modules. Because of their shape, the modules completely fill the plane without any gaps, their centers forming a regular lattice. Both the hexagonal and square modules are provided with electromechanical coupling mechanisms actuated by D.C. motors. These connectors help to couple and uncouple modules as they move around each other to form different configurations. The modules are currently controlled by an external processor

In this article we examine the problem of dynamic self-reconfiguration of a class of modular robotic systems referred to as metumorpkic systems. A metamorphic robotic system is a collection of mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules. A change in the macroscopic morphology results from the locomotion of each module over its neighbors. Metamorphic systems can therefore be viewed as a large swarm of physically connected robotic modules that collectively act as a single entity. What distinguishes metamorphic systems from other reconfigurable robots is that they possess all of the following properties: (1) a large number of homogeneous modules; (2) a geometry such that modules fit within a regular lattice; (3) self-reconfigurability without outside help; (4) physical constraints which ensure contact between modules. In this article, the kinematic constraints governing metamorphic robot self-reconfiguration are addressed, and lower and upper bounds are established for the minimal number of moves needed to change such systems from any initial to any final specified configuration. These bounds are functions of initial and final configuration geometry and can be computed very quickly, while it appears that solving for the precise number of minimal moves cannot be done in polynomial time. It is then shown how the bounds developed here are useful in evaluating the performance of heuristic motion planning/reconfiguration algorithms for metamorphic systems. 0 2996 Iohn Wiky 6 Sons, rnc. *To whom all correspondence should be addressed

Real-world applications that are ideal for robotic solutions are very complex and challenging. Many of these applications are set in dynamic environments that require capabilities distributed in functionality, space, or time. These applications, therefore, often require teams of robots to work together cooperatively to successfully address the mission. While much research in recent years has addressed the issues of autonomous robots and multi-robot cooperation, current robotics technology is still far from achieving many of these real world applications. We believe that two primary reasons for this technology gap are that (1) previous work has not adequately addressed the issues of fault tolerance and adaptivity in multi-robot teams, and (2) existing robotics research is often geared at specific applications, and is not easily generalized to different, but related, applications. This paper addresses these issues by first describing the design issues of key importance in these real-world cooperative robotics applications -- fault tolerance, reliability, adaptivity, and coherence. We then present a general architecture addressing these design issues -- called ALLIANCE -- that facilitates multi-robot cooperation of small- to mediumsized teams in dynamic environments, performing missions composed of loosely coupled subtasks. We illustrate the generality of this architecture by describing two very different proof-of-concept implementations of this architecture: a janitorial service mission, and a bounding overwatch mission.

We are developing a materials handling system where many small simple actuators cooperate to transport and to manipulate large objects in the plane. A discrete set of cells, each comprising two actuators, are fixed in a planar array. By coordinating the actuators in the cells on which an objects rests, an object can be transported and manipulated. In essence, this system is an improvement over traditional conveyor systems in that objects can be re-oriented, as well as conveyed. Such an array provides flexible materials handling in which many objects independently can be manipulated and transported at the same time. The array is coordinated in a distributed manner where each cell has its own controller and each controller communicates with its neighbors. Towards the goal of motion planning, in this paper we consider the dynamics of parcel transport and manipulation. The parcel dynamics are based on an exact discrete representation of the system, unlike other methods where a continuity a...

The aim of this paper is to discuss and present some experimental data on the use of optical, chemical and ultrasound sensors to control the motion of single and cooperating robots. The distinctive feature is that in both cases the motion of the robots is not driven by a localized target (e.g. a light, or a sound) but by a concentration gradient such as the one generated by a gas leak. Experiments using a simple optical sensor reading a variable density pattern of dark spots painted on the ground plane, is presented along with experiments performed using simple chemical sensors measuring the local gradient of a substance. Different strategies will be discussed based upon different configurations of the sensing devices and reflexive motor controls. The goal of the experiment is to analyze the performance of simple autonomous robots in reaching the point of highest concentration (i.e. the gas leak). This performance has been analyzed for single robots and for cooperating robots using a v...

. Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment. 1.

In practical applications of robotics, it is usually quite difficult, if not impossible, for the system designer to fully predict the environmental states in which the robots will operate. The complexity of the problem is further increased when dealing with teams of robots which themselves may be incompletely known and characterized in advance. It is thus highly desirable for robot teams to be able to adapt their performance during the mission due to changes in the environment, or to changes in other robot team members. In previous work [40, 44], we introduced a behavior-based mechanism --- called the ALLIANCE architecture --- that facilitates the fault tolerant cooperative control of multi-robot teams. However, this previous work did not address the issue of how to dynamically update the control parameters during a mission to adapt to ongoing changes in the environment or in the robot team, and to ensure the efficiency of the collective team actions. In this paper, we address this iss...