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Mark Moors

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5 papers

1 author row

IROS Conference 2007 Conference Paper

Accompanying persons with a mobile robot using motion prediction and probabilistic roadmaps

Frank Hoeller
Dirk Schulz 0001
Mark Moors
Frank E. Schneider

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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IROS Conference 2006 Conference Paper

Improved Markov Models for Indoor Surveillance

Mark Moors
Dirk Schulz 0001

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 intruder's 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

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IROS Conference 2005 Conference Paper

A probabilistic approach to coordinated multi-robot indoor surveillance

Mark Moors
Timo Röhling
Dirk Schulz 0001

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.

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ICRA Conference 2004 Conference Paper

Methods and Experiments for Hazardous Area Activities using a Multi-robot System

Frank E. Schneider
Dennis Wildermuth
Mark Moors

This work presents new methods and experiments for hazardous area activities using a multi robot system. In a special test environment various dangerous settings are reproduced. The experiments include the scanning for hazardous material and radiation. The paper will present empirical results on collective mapping of the sensor information. In addition, an approach to the problem of relative position estimation for multi robot systems is presented. The sensor information of the robots is utilized to estimate the relative positions between each other. An Extended Kalman Filter (EKF) is used to combine the gathered position information into one continuously updated position estimation. All robots of a group use these data in order to generate one common co-ordinate system. This co-ordinate system is a "relative" one, meaning that it has no fixed reference to global world co-ordinates. Preliminary results of experiments with real robots are presented.

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ICRA Conference 2000 Conference Paper

Collaborative Multi-Robot Exploration

Wolfram Burgard
Mark Moors
Dieter Fox
Reid G. Simmons
Sebastian Thrun

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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