Abstract-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.

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 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.

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To ensure the safety of people, it is important that mobile robots operating in populated environments are able to take the motions of humans in their vicinity into account. An especially demanding task in this respect is accompanying a person walking through an unknown and busy environment, because it requires the robot to stay close to his client and simultaneously prevent bumping into any passers-by. This paper presents a local navigation planning approach for collision avoidance, which aims at achieving this goal. The presented technique uses probabilistic roadmaps to plan collision-free paths to a given target location relative to the robot. A laser-based people tracking component is used to estimate the motions of humans in the robot's surrounding, and a potential field method is applied for predicting the humans' future trajectories based on this information. In addition to preventing collisions, the predictions enable us to choose appropriate target locations relative to the person being attended. We tested our method on real robots and in simulations. The experiments carried out in an office environment confirm that the integrated motion prediction actually improves the performance of the collision avoidance and the robot's ability to stay close to the client it accompanies.

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