Mark Moors

Coordinated multi-robot exploration

In this paper, we consider the problem of exploring an unknown environment with 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 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.

Collaborative multi-robot exploration

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.

Collaborative Exploration of Unknown Environments with Teams of Mobile Robots

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.

A probabilistic approach to coordinated multi-robot indoor surveillance

In this paper we discuss the problem of monitoring and searching an indoor environment for an intruder with a group of mobile robots. We present a graph-based algorithm to coordinate a group of robots which takes the limitations and uncertainties of sensors into account and is able to find good coordination plans efficiently even for large environments. We analyze and compare the approach against other coordination strategies based on a new probabilistic framework that allows to evaluate the performance of any coordination strategy based on a probabilistic sensor model and a worst case behavior model for intruders. Using this framework we demonstrate the capabilities of the planning algorithm in several simulation experiments.

Tele-Presence in Populated Exhibitions Through Web-Operated Mobile Robots

This paper presents techniques that facilitate mobile robots to be deployed as interactive agents in populated environments such as museum exhibitions or trade shows. The mobile robots can be tele-operated over the Internet and, this way, provide remote access to distant users. Throughout this paper we describe several key techniques that have been developed in this context. To support safe and reliable robot navigation, techniques for environment mapping, robot localization, obstacle detection and people-tracking have been developed. To support the interaction of both web and on-site visitors with the robot and its environment, appropriate software and hardware interfaces have been employed. By using advanced navigation capabilities and appropriate authoring tools, the time required for installing a robotic tour-guide in a museum or a trade fair has been drastically reduced. The developed robotic systems have been thoroughly tested and validated in the real-world conditions offered in the premises of various sites. Such demonstrations ascertain the functionality of the employed techniques, establish the reliability of the complete systems, and provide useful evidence regarding the acceptance of tele-operated robotic tour-guides by the broader public.

Improved Markov Models for Indoor Surveillance

In this paper we look at the problem of searching a human intruder in a closed environment with a small group of mobile robots. In this context motion models for the intruder play an important role for planning the coordination of the robots. Often, simple Brownian motion models are used for this purpose. However, the assumed completely random change of direction in each time step is very unrealistic. We present an improved Markovian motion model that takes the intended motion direction of a person into account in order to achieve a more realistic motion prediction. This model is then used to estimate a probability distribution of an intruders location within the environment. We develop a greedy algorithm that employs this distribution to coordinate the search of the environment by a group of robots. Finally, we compare our algorithm to two simple search methods and evaluate its behavior in simulation experiments