In the context of mobile robot path planning, a common strategy is to assume that the world is static and rely on heuristic approaches and obstacle avoidance to deal with the changes in the environment. When planning, not taking the potential changes of the environment into account usually leads to poor performances. In this paper we propose a probabilistic model that explicitly characterizes the traversability of the environment as a stochastic process. Furthermore, we present a path planning approach that exploits this traversability information to compute paths that minimize the expected travel time of the robot. Experimental results show that by explicitly modeling and reasoning about changes in the environment path planning performance can be improved.

The majority of existing approaches to mobile robot mapping assumes that the world is static, which is generally not justified in real-world applications. However, in many navigation tasks including trajectory planning, surveillance, and coverage, accurate maps are essential for the effective behavior of the robot. In this paper we present a probabilistic grid-based approach for modeling changing environments. Our method represents both, the occupancy and its changes in the corresponding area where the dynamics are characterized by the state transition probabilities of a Hidden Markov Model. We apply an offline and an online technique to learn the parameters from observed data. The advantage of the online approach is that it can dynamically adapt the parameters and at the same time does not require storing the complete observation sequences. Experimental results obtained with data acquired by real robots demonstrate that our model is well-suited for representing changing environments. Further results show that our technique can be used to substantially improve the effectiveness of path planning procedures.

Robust and reliable localization is a fundamental prerequisite for many applications of mobile robots. Although there exist many solutions to the localization problem, structurally symmetrical or featureless environments can prevent different locations from being distinguishable given the data obtained with the robot's sensors. Such ambiguities typically make localization approaches more likely to fail. In this paper, we investigate how artificial landmarks can be utilized to reduce the ambiguity in the environment. We present a practical approach to compute a configuration of indistinguishable landmarks that decreases the overall ambiguity and thus increases the robustness of the localization process. We evaluate our approach in different environments based on real data and in simulation. Our results demonstrate that our approach improves the localization performance of the robot and outperforms other landmark selection approaches.

Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map configuration according to environmental constraints (including human whereabouts) and the demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is described by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.

Accurate and robust localization is essential for the successful navigation of autonomous mobile robots. The majority of existing localization approaches, however, is based on the assumption that the environment is static which does not hold for most practical application domains. In this paper, we present a localization framework that can robustly track a robot's pose even in non-static environments. Our approach keeps track of the observations caused by unexpected objects in the environment using temporary local maps. It relies both on these temporary local maps and on a reference map of the environment for estimating the pose of the robot. Experimental results demonstrate that by exploiting the observations caused by unexpected objects our approach outperforms standard localization methods for static environments.

To act intelligently in dynamic environments, a system must understand the current situation it is involved in at any given time. This requires dealing with temporal context, handling multiple and ambiguous interpretations, and accounting for various sources of uncertainty. In this paper we propose a probabilistic approach to modeling and recognizing situations. We define a situation as a distribution over sequences of states that have some meaningful interpretation. Each situation is characterized by an individual hidden Markov model that describes the corresponding distribution. In particular, we consider typical traffic scenarios and describe how our framework can be used to model and track different situations while they are evolving. The approach was evaluated experimentally in vehicular traffic scenarios using real and simulated data. The results show that our system is able to recognize and track multiple situation instances in parallel and make sensible decisions between competing hypotheses. Additionally, we show that our models can be used for predicting the position of the tracked vehicles.

In this paper, we present an approach for learning generalized models for traffic situations. We formulate the problem using a dynamic Bayesian network (DBN) from which we learn the characteristic dynamics of a situation from labeled trajectories using kernel regression. For a new and unlabeled trajectory, we can then infer the corresponding situation by evaluating the data likelihood for the individual situation models. In experiments carried out on laser range data gathered on a car in real traffic and in simulation, we show that we can robustly recognize different traffic situations even from trajectories corresponding to partial situation instances.