The objective of this paper is to bring an effective response to the safe path planning problem which should be solved in an uncertain-configuration space. Firstly, a path planning method dealing with localization uncertainties is proposed, where the uncertainties in both position and orientation of a non-holonomic mobile robot are considered. The safety of this method is due to the mixing of the planning phase and the naviyation phase using the same process of localization (the Kalman filter). Secondly, while previous works planned safe paths in the configuration space, we show that it is nece s s a y to plan safe paths in an uncertain-configuration space. Then, we introduce the novel concept of "towers of uncertainties" and show the effectiveness of this concept with some examples.0-7803-7736-2/03/$17.00 02003 IEEE

Abstract-In order to navigate safely, it is important to detect and to react to a potentially dangerous situation. Such a situation can be underlined by a judicious use of the locations and the uncertainties of both the navigating vehicle and the obstacles. We propose to build an estimation of the collision probability from the environment perception with its probabilistic modeling. The probability of collision is computed from a sum of integral of a product of Gaussians. The integrals takes into account the uncertain configurations and the volume of both the vehicle and the obstacles.

This paper addresses the problem of path plannrng using a dynamic vehicle model. Previous works which include a basic kinematic model generate paths that are only realistic at very low speed. By considering higher vehicle speed during navigation, the vehicle can significantly deviate from the planned trajectory. Consequently, the planned path becomes unusable for the mission achievement. So, to bridge a gap between planning and navigation, we propose a realistic path planner based on a dynamic vehicle model.

This paper addresses the problem of safe path planning in an uncertain-configuration space. We consider the case of a car-like robot moving in an indoor environment (three-dimensional space). The Extended Kalman Filter (EKF) is a popular way to localize such a robot and to estimate its configuration uncertainty during navigation. Consequently, we supply an EKF with simulated measurements in order to compute realistic uncertainties (in a four-dimensional space) during path planning. We show that our Safe-RRT algorithm, based upon Rapidly-exploring Random Trees (RRT), is an efficient way to find a path in the resulting seven-dimensional uncertainconfiguration space.

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Estimating the configuration of a vehicle is crucial for its navigation. Most approaches are based on (extended) Kalman filtering or particle filtering. An attractive alternative is considered here, which relies on interval analysis. Contrary to classical extended Kalman filtering it allows global localization, and contrary to particle filtering it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. This paper presents a real-time implementation of the process including a description of the platform and its modeling, the integration of the errors on the model and the localization method itself.

Abstract-Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are Kalman filtering and Bayesian localization, often implemented via particle filtering. This paper reports on-going experimentation with an attractive alternative approach recently developed and based on interval analysis. Contrary to classical Extended Kalman Filtering, this approach allows global localization, and contrary to Bayesian localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. The approach is particularly robust to outliers.