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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 modelling. The probability of collision...
The estimation of a vehicle configuration in its environment is mostly solved by Bayesian methods. Interval analysis allows an alternative approach: bounded-error localization. Such an approach provides a bounded set of configuration that is guaranteed to surround the actual vehicle configuration. This method is validated in real time through experimental results on a mobile robot in an indoor environment.
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 modelling. Then this probability...
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...
The object of this paper is firstly to describe a safe path planning algorithm in an uncertain-configuration space named SATU*, based on one of the most used path planner, the A* algorithm, and to give its proofs of optimality and completeness. Secondly, it is to present an application of our algorithm to a mobile robotic problem, the way we tested the SATU* in a simulated environment close to the...
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