Finding Solvable Priority Schemes for Decoupled Path Planning Techniques for Teams of Mobile Robots

. Coordinating the motion of multiple mobile robots is one of the fundamental problems in robotics. The predominant algorithms for coordinating teams of robots are decoupled and prioritized, thereby avoiding combinatorially hard planning problems typically faced by centralized approaches. In this paper we present a method for finding solvable priority schemes for such prioritized and decoupled planning techniques. Existing approaches apply a single priority scheme which makes them overly prone to failure in cases where valid solutions exist. By searching in the space of priorization schemes, our approach overcomes this limitation. To focus the search, our algorithm is guided by constraints generated from the task specification. To illustrate the appropriateness of this approach, this paper discusses experimental results obtained with real robots and through systematic robot simulation. The experimental results demonstrate that our approach can successfully solve many more coordination problems than previous decoupled and prioritized techniques.

Adapting Navigation Strategies Using Motions Patterns of People

As people move through their environments, they do not move randomly. Instead, they are often engaged in typical motion patterns, related to specific locations they might be interested in approaching. In this paper we propose a method for adapting the behavior of a mobile robot according to the activities of the people in its surrounding. Our approach uses learned models of people's motion behaviors. Whenever the robot detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in. During path planning it then uses this belief to improve its navigation behavior. In different practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adapt its navigation plans according to the activities of the persons in its surrounding. We also present experiments illustrating that our approach provides a better behavior than a standard reactive collision avoidance system

Active Localization of People with a Mobile Robot Based on Learned Motion Behaviors

Mobile robots that provide service to people can carry out their tasks more efficiently if they know where the people are. In this paper we present an approach to actively maintain a probabilistic belief about the current locations of people in the environment of a mobile robot. We assume that the robot is equipped with knowledge about typical motion behaviors of people in form of Hidden Markov Models (HMMs), which are updated based on vision and laser information. While the robot is carrying out its task it applies a decision-theoretic approach to actively select points in the environment that are expected to provide information about the positions of people. Experimental results obtained with a mobile robot in a typical office environment illustrate that our method decreases the uncertainty about the positions of people compared to passive approaches which do not consider additional observation actions.