Jean-Loup Farges

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Possibilistic and qualitative POMDPs (πPOMDPs) are counterparts of POMDPs used to model situations where the agent’s initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection. However, like probabilistic POMDPs, optimally solving πPOMDPs is intractable: the finite belief state space(More)
Autonomous mobile robots such as planetary rover need task and path planning abilities in order to fulfill their assigned missions. Task planning is caracterized by a symbolic reasoning and aims at defining a sequence of actions which will be executed to achieve the goals of the mission. Path planning allows to find some ways in the environment to reach(More)
The approach presented in this paper aims at finding a solution to the problem of conflict-free motion planning for multiple aircraft on the same flight level with trajectory recovery. One contribution of this work is to develop three consistent models, i.e., from a continuous-time representation to a discrete-time linear approximation. Each of these models(More)
Qualitative Possibilistic Mixed-Observable MDPs (πMOMDPs), generalizing π-MDPs and π-POMDPs, are well-suited models to planning under uncertainty with mixed-observability when transition, observation and reward functions are not precisely known and can be qualitatively described. Functions defining the model as well as intermediate calculations are valued(More)
This article presents the integration of on-line mission planning and flight scheduling for an unmanned aerial vehicle in military observation missions. Planning selects and orders the best subset of observations to be carried out and schedules the observations while accommodating time windows. The vehicle is subjected to speed, fuel supply and flight(More)