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

In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved therefore is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of their environment. We present a probabilistic approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the costs of reaching a target point and the utility of target points. The utility of target points is given by the size of the unexplored area that a robot can cover with its sensors upon reaching a target position. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has...

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.

This paper addresses the problem of exploration and mapping of an unknown environment by multiple robots. The mapping algorithm is an on-line approach to likelihood maximization that uses hill climbing to find maps that are maximally consistent with sensor data and odometry. The exploration algorithm explicitly coordinates the robots. It tries to maximize overall utility by minimizing the potential for overlap in information gain amongst the various robots. For both the exploration and mapping algorithms, most of the computations are distributed. The techniques have been tested extensively in real-world trials and simulations. The results demonstrate the performance improvements and robustness that accrue from our multirobot approach to exploration.

Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. 1

This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. The proposed approach improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reectance properties. Two dierent algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots. Keywords: Exploration, Mapping, Multiple Robots, Cooperative Localization. 1. Introduction In this paper we discuss the benets of cooperative localization during the exploration of a large environment. A new

In this paper, we consider the problem of exploring an unknown environment with a team of robots. As in singlerobot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots, which simultaneously takes into account the cost of reaching a target point and its utility. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced. In this way, different target locations are assigned to the individual robots. We furthermore describe how our algorithm can be extended to situations in which the communication range of the robots is limited. Our technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission.

. This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots. The robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. 1 Introduction Sensor-based robot localization has been recognized as one ...

This paper presents Internal Position Error Correction (IPEC) --- a new method for accurate and reliable dead-reckoning with mobile robots. The IPEC method has been implemented on our recently developed Multi-Degree-of-Freedom (MDOF) mobile platform, a vehicle in which two differential-drive mobile robots (called "trucks") are physically connected through a compliant linkage. In addition to its four wheel encoders, the MDOF platform has one linear and two rotary internal encoders, which allow measurement of the relative distance and bearing between the two trucks. During operation, both trucks perform conventional dead-reckoning with their wheel encoders. But, in addition, the IPEC method uses information from the internal encoders to detect and correct dead-reckoning errors as soon as they occur. Our system, called Compliant Linkage Autonomous Platform with Position Error Recovery (CLAPPER), requires neither external references (such as navigation beacons, artificial landmarks, known floorplans, or satellite signals), nor inertial navigation aids (such as accelerometers or gyros). Nonetheless, the experimental results included in this paper show one to two orders of magnitude better positioning accuracy than systems based on conventional dead-reckoning. The CLAPPER corrects not only systematic errors, such as different wheel diameters, but also non-systematic errors, such as those caused by floor roughness, bumps, or cracks in the floor. These features are made possible by exploiting the new Growth-Rate Concept for deadreckoning errors that is introduced in this paper for the first time. The Growth-Rate Concept distinguishes between certain dead-reckoning errors that develop slowly while other deadreckoning errors develop quickly. Based on this concept, truck A freque...

In this paper we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing each other. Each of the robots collects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning information from all the members of the team and produces a pose estimate for each of them. The equations for this centralized estimator can be written in a decentralized form therefore allowing this single Kalman filter to be decomposed intoanumber of smaller communicating filters each of them processing local (regarding the particular host robot) data for most of the time. The resulting decentralized estimation scheme constitutes a unique mean for fusing measurements collected from a variety of sensors with minimalcommunication and processing requirements. The distributed localization algorithm is applied to a group o...