Toward reliable and robust path planning for autonomous systems
type: Dataset
description: Many tools exist to solve constrained path-planning problems. They can be classified as follows. In the older ones, paths are planned in a discretized state space. The most recent, sampling-based path planners can explore the whole state space more efficiently. These path planners are used in many fields, e;g., chemistry, biology, automatic control or robotics. The main contribution of our work is to provide a solution to the path planning problem when uncertainty is present. Modern planning techniques are used in combination with localization algorithms that make it possible to characterize the uncertainty on the system state at a given time. Two approaches are considered. In the first one, the path planner uses a probabilistic representation of the state space at any given time using multivariate Gaussian distributions. An extended Kalman filter is used to propagate the state error. In the second approach, all states that are consistent with bounds on the errors are enclosed in a computable set. Contrary to the previous probabilistic method, this one is able to guarantee the safety of the system moving along the planned path, provided of course that the hypotheses on which it is based are satisfied.
issued: 2026-05-10T07:51:11.195103
language: fr
modified: 2026-05-10T07:51:11.195107
contact point: Organization
  fn: CCSD
keyword: automatique
keyword: incertitudes
keyword: infoeu-reposemanticsdoctoralthesis
keyword: planification
keyword: robotique
keyword: spiautoengineering-sciences-physicsautomatic
keyword: theses
landing page: https://theses.hal.science/tel-00845477

Toward reliable and robust path planning for autonomous systems
type: Distribution
format: HTML
issued: 2026-05-10T07:51:11.202850
modified: 2026-05-10T07:51:11.187492
access URL: https://theses.hal.science/tel-00845477

test_moissonnage_selune
type: Agent