Abstract
The performance is a multi-form concept widely defined in manufacturing robotics with specific environment conditions (static and perfectly known) and infinite energy).However, performance indicators proposed in mobile robotics are less widely accepted.We differenciate between main performance axes (energy, safety, localization and stability) and secondary performance axes (duration for example).In our work, we are interested on missions realized under duration, safety and energy performance constraints, in a known but dynamic environment.Applying the different constraints decomposes the mission into a sequence of activities realized under invariant constraints.Each one, can be realized by a set of robotic tasks (move, be located, analyze an image, etc.).These tasks can be implemented in various ways according to the different possibles actuators, sensors and algorithms configurations.The adressed problem is the following: how to choose the hardware and software resources to use along a mission while satisfying the different performance constraints ? It is a multicriteria knapsack problem known to be NP-hard, where the complexity becomes very quickly unexplorable.To propose and guarantee an applicable solution under real-time constraints, we used an algorithm allowing to find a set of good solutions in few iterations.The proposed resources management approach is implemented on a Pioneer-3DX robot using a control architecture based on the Middleware ContrACT.This approach has been validated on a patrolling mission travelling 200 m within the LIRMM laboratory during about 10 mn, to verify the state of valves.For the considered mission, the state space dimension is higher than 10^{14}.The hardware and software resources are dynamically and autonomously selected along the mission to satisfy the different performance constraints.If a resource becomes faulty and/or many obstacle avoidances occure and lead to performance drift, the developed approach finds on line a new resources allocation solution (if it exists).So this approach allows to enhance the fault tolerance of the robotic system.