Bruno Lacerda

Optimal and dynamic planning for Markov decision processes with co-safe LTL specifications.

We present a method to specify tasks and synthesise 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 cost to satisfy the conjunction of the current tasks and the new one, taking into account how much of the current tasks has already been executed. We illustrate our approach by applying it to motion planning for a mobile service robot.

The STRANDS Project: Long-Term Autonomy in Everyday Environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.

Now or later? Predicting and maximising success of navigation actions from long-term experience

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 robots chances of successively accomplishing actions in this future. Hence, a robots plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.

Designing Petri Net Supervisors from LTL Specifications

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 Buchi automaton representing the LTL formula and trimming the result using a known method to reduce the size of the supervisor. Furthermore, we guarantee that the obtained supervisors are admissible by construction by restricting the LTL formulas that can be written to an appropriate subset. To illustrate the method, we provide an example on how to specify coordination rules for a team of simulated soccer robots.

An integrated control framework for long-term autonomy in mobile service robots

This paper describes an integrated framework for the long-term task-driven control of mobile service robots. The core components of the framework are: a high-level task executor that manages execution, for example by reacting to failures, or adding extra tasks required by the end-user on-the-fly; a task scheduler that schedules sets of tasks throughout the day, taking into account travel times between locations and task durations, while satisfying the time constraints associated with each task; and a probabilistic topological motion planner that provides time-dependent optimal navigation policies and expected navigation times between task locations. We illustrate the overall framework by reporting on a three-week deployment in a real-world office environment, and use the data collected during the deployment to validate and illustrate the capabilities of the framework to adapt itself to the different travel time expectations throughout the day.

On the Notion of Uncontrollable Marking in Supervisory Control of Petri Nets

We show that the notion of uncontrollable marking commonly used in the literature on supervisory control theory of Petri nets is not sound, by means of a counter-example. We also show how the definition can be corrected and provide an adaptation of a decidability proof for the problem of checking controllability for specifications expressed as deterministic Petri net languages.

LTL-based decentralized supervisory control of multi-robot tasks modelled as petri nets

We present a decentralized methodology to control multi-robot systems, where each robot behaviour is modelled as a Petri net (PN) and a set of coordination rules between the robots is given as linear temporal logic (LTL) formulas describing safety properties for the system. The LTL formulas are used to define the events and changes in state that must be communicated between robots and to augment the individual PN model of each robot so that it can handle the incoming communications. These augmented PNs are then used, in conjunction with the LTL formulas, to build PN realizations of local supervisors, based on discrete event system theory, that enforce the LTL specifications by construction. The methodology is illustrated through a simulated application example.