Bruno Lacerda

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—We present a methodology to build a Petri net realization of a supervisor that, given a Petri net model of a (multi-)robot system and a linear temporal logic (LTL) specification , forces the system to fulfil the specification. The methodology includes composing the Petri net model with the B ¨ uchi automaton representing the LTL formula and trimming the(More)
— We present a method to specify tasks and synthe-sise cost-optimal policies for Markov decision processes using co-safe linear temporal logic. Our approach incorporates a dynamic task handling procedure which allows for the addition of new tasks during execution and provides the ability to re-plan an optimal policy on-the-fly. This new policy minimises the(More)
We present a method to calculate cost-optimal policies for task specifications in co-safe linear temporal logic over a Markov decision process model of a stochastic system. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We formalise a task progression metric and, using multi-objective probabilistic(More)
In this paper, we use LTL to specify acceptable/desirable behaviours for a system modelled as a Petri net, and create a Petri net realization of a supervisor that is guaranteed to enforce them, by appropriately restricting the uncontrolled behaviour of the system.We illustrate the method with an application to the specification of coordination requirements(More)
— In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its(More)
Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial(More)
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