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 we report on our recent work evolving controllers for robots which are required to work as a team. The word team ' has been used in a variety of senses in both the multi-robot and the ethology literature, so it is appropriate to start the paper with a denition. We will adopt the denition given by Anderson & Franks (2001) in their recent review of team behaviour in animal societies. They identify three dening features of team behaviour. First, individuals make dierent contributions to task success, i.e. they must perform dierent sub-tasks or roles (this does not preclude more than one individual adopting the same role; there may be more individuals than roles). Second, individual roles or sub-tasks are interdependent (or interlocking'), requiring structured cooperation; individuals operate concurrently, coordinating their dierent contributions in order to complete the task. Finally, a team's organizational structure persists over time, although its individuals may be substituted or swap roles (Anderson & Franks 2001)

We present multi-agent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA * has significant advantages. We introduce an anytime variant of MAA * and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems. 1

Role assignment and coordination are difficult issues for multi-robot systems, especially in highly dynamic tasks. Robot soccer is one such task and it provides a unique challenge for multi-robot research. In this paper, we contribute the approach that we successfully developed for CMPACK'02 , our team for the RoboCup-2002 Sony legged league. The RoboCup-2002 Sony robots were equipped with wireless communication for the first time this year. We developed an approach for sharing sensed information and effective coordination through the introduction of shared potential fields. The potential fields were based on the positions of the other robots on the team and the ball. The robots positioned themselves on the field by following the gradient to a minimum of the potential field. In principle, our potential functions can be applied to any multi-robot domain. We present the results of the RoboCup-2002 competition, which we won, and we show a post-competition, controlled empirical evaluation to analyze the impact of our algorithm. The results demonstrate that our team using our communication-dependent coordination outperforms a team of individually skilled robots without coordination.

In the domain of decentralized Markov decision processes, we develop the first complete and optimal algorithm that is able to extract deterministic policy vectors based on finite state controllers for a cooperative team of agents. Our algorithm applies to the discounted infinite horizon case and extends best-first search methods to the domain of decentralized control theory. We prove the optimality of our approach and give some first experimental results for two small test problems. We believe this to be an important step forward in learning and planning in stochastic multi-agent systems.

Communication among a group of robots should in principle improve the overall performance of the team of robots, as robots may share their world views and may negotiate task assignments. However, in practice, effectively handling in real-time multi-robot merge of information and coordination is a challenging task. In this paper, we present the approach that we have successfully developed for a team of communicating soccer robots acting in a highly dynamic environment. Our approach involves creating shared potential functions based on shared positions of relevant obstacles in the world. The biases introduced in the potential functions are general and they could in principle be provided by external sources, such as a human or robot coach. We provide controlled experiments to analyze the impact of our approach in the overall performance of a robot team.

Abstract. 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 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, in contrast to previous approaches, simultaneously takes into account the cost of reaching a target point and its utility. The utility of a target point is given by the size of the unexplored area that a robot can cover with its sensors upon reaching that location. 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 been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches. 1

In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. One of the key issues in multi-robot exploration is how to assign target locations to the individual robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). To determine the type of a place, we apply a classifier learned with AdaBoost which additionally considers spatial dependencies between nearby locations. Our approach to incorporate the type of places in the coordination of the robots has been implemented and tested in di#erent environments. The experiments demonstrate that our system e#ectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the semantic place labels.

Abstract — This paper addresses the problem of exploring an unknown environment with a team of mobile robots. The key issue in coordinated multi-robot exploration is how to assign target locations to the individual robots such that the overall mission time is minimized. In this paper, we propose a novel approach to distribute the robots over the environment that takes into account the structure of the environment. To achieve this, it partitions the space into segments, for example, corresponding to individual rooms. Instead of only considering frontiers between unknown and explored areas as target locations, we send the robots to the individual segments with the task to explore the corresponding area. Our approach has been implemented and tested in simulation as well as in real world experiments. The experiments demonstrate that the overall exploration time can be significantly reduced by considering our segmentation method. I.

Contents Chapter 1 - Introduction: A minimalistic representational approach 9 1.1 | Robot interaction 11 1.2 | Niche environments 12 1.3 | The need for representations 12 Chapter 2 - The world of Unies and the Wumpus 17 2.1 | Starting Conditions 18 2.2 | Bodily properties of the Unies and the Wumpus 19 2.3 | Environments 20 2.3.1 | Sample Environments 20 2.4.1 | Behavior and the design of the Unies and the Wumpus 22 2.4.2 | Behavior depending on the energy state of the Unies 23 v 2.4.3 | Obstacles in the environment 24 2.4.4 | Behavior in the rain forest environment 26 2.4.5 | Behavior in the forest environment 27 2.4.6 | Behavior in the plains environment 27 2.4.7 | Behavior in the desert environment 28 2.4.8 | Decoy behavior in our environments 28 2.5 | Cooperative behavior between Unies 28 Chapter 3 - The subsumption architecture 31 3.1 | Minimalism, statelessness, and tolerance 32 3.2 | Brooks' subsumption architecture 32 3.3 | Designing the Unies and the