Cooperative Task Planning of Multi-Agent Systems Under Timed Temporal Specifications

In this paper the problem of cooperative task planning of multi-agent systems when timed constraints are imposed to the system is investigated. We consider timed constraints given by Metric Interval Temporal Logic (MITL). We propose a method for automatic control synthesis in a two-stage systematic procedure. With this method we guarantee that all the agents satisfy their own individual task specifications as well as that the team satisfies a team global task specification.Comment: Submitted to American Control Conference 201

Using Formal Methods for Autonomous Systems: Five Recipes for Formal Verification

Formal Methods are mathematically-based techniques for software design and engineering, which enable the unambiguous description of and reasoning about a system's behaviour. Autonomous systems use software to make decisions without human control, are often embedded in a robotic system, are often safety-critical, and are increasingly being introduced into everyday settings. Autonomous systems need robust development and verification methods, but formal methods practitioners are often asked: Why use Formal Methods for Autonomous Systems? To answer this question, this position paper describes five recipes for formally verifying aspects of an autonomous system, collected from the literature. The recipes are examples of how Formal Methods can be an effective tool for the development and verification of autonomous systems. During design, they enable unambiguous description of requirements; in development, formal specifications can be verified against requirements; software components may be synthesised from verified specifications; and behaviour can be monitored at runtime and compared to its original specification. Modern Formal Methods often include highly automated tool support, which enables exhaustive checking of a system's state space. This paper argues that Formal Methods are a powerful tool for the repertoire of development techniques for safe autonomous systems, alongside other robust software engineering techniques.Comment: Accepted at Journal of Risk and Reliabilit

Formal synthesis of control and communication schemes

Thesis (Ph.D.)--Boston UniversityIn traditional motion planning, the problem is simply specified as "go from A to B while avoiding obstacles", where A and B are two configurations or regions of interest in the robot workspace. However, a large number of robotic applications require more expressive specification languages, which allow for logical and temporal statements about the satisfaction of properties of interest. Examples include "visit A and B infinitely often, always avoid C, and do not visit D unless E vas visited before". Such task specifications cannot be trivially converted to a sequence of "go from A to B" primitives. This thesis establishes theoretical and computational frameworks for automatic synthesis of robot control and communication schemes that are correct-by-construction from task specifications given in expressive languages. We consider a purely discrete scenario, in which the dynamics of each robot is modeled as a finite discrete system. The first problem addressed in this thesis is the generation of provably-correct individual control and communication strategies for a team of robots from rich task specifications in the case when the workspace is static. The second problem relaxes this assumption and considers a scenario in which the environment changes according to some unknown patterns. It proposed a combined learning and formal synthesis approach to generate correct control policies. To tackle the first problem, we draw inspirations from the research fields of formal verification and synthesis, distributed formal synthesis, and concurrency theory. We consider a team of robots that can move among the regions of a partitioned environment and have known capabilities of servicing a set of requests that can occur in the regions of the partition. Some of these requests can be serviced by a robot individually, while some require the cooperation of groups of robots. We propose a top-down approach, in which global specifications given as Regular Expressions (RE) or Linear Temporal Logics (LTL) can be decomposed into local (individual) specifications, which can then be used to automatically synthesize robot control and communication strategies. To address the second problem, we bring together automata learning methods from the field of theoretical linguistics and techniques from temporal logic games and probabilistic model checking, to develop a provably-correct control strategy for robots moving in an environment with unknown dynamics. The robots are required to achieve a surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time in between consecutive services is minimized and additional temporal logic constraints are satisfied. We define a fragment of Linear Temporal Logic (LTL) to describe such a mission. We consider a single agent case at first and then extend the results to multi-agent systems. To this end, we apply approximate dynamic programming to our computational framework, which leads to significant reduction of computational time. To demonstrate the proposed theoretical and computational frameworks, we implement the derived algorithms in two experimental platforms, the Robotic Urban-Like Environment (RULE) and the Robotic InDoor-like Environment (RIDE). We assign tasks to the team using Regular Expressions or Linear Temporal Logics over requests occurring at regions in the environment. The robots are automatically deployed to complete the missions

Formal methods paradigms for estimation and machine learning in dynamical systems

Formal methods are widely used in engineering to determine whether a system exhibits a certain property (verification) or to design controllers that are guaranteed to drive the system to achieve a certain property (synthesis). Most existing techniques require a large amount of accurate information about the system in order to be successful. The methods presented in this work can operate with significantly less prior information. In the domain of formal synthesis for robotics, the assumptions of perfect sensing and perfect knowledge of system dynamics are unrealistic. To address this issue, we present control algorithms that use active estimation and reinforcement learning to mitigate the effects of uncertainty. In the domain of cyber-physical system analysis, we relax the assumption that the system model is known and identify system properties automatically from execution data. First, we address the problem of planning the path of a robot under temporal logic constraints (e.g. "avoid obstacles and periodically visit a recharging station") while simultaneously minimizing the uncertainty about the state of an unknown feature of the environment (e.g. locations of fires after a natural disaster). We present synthesis algorithms and evaluate them via simulation and experiments with aerial robots. Second, we develop a new specification language for tasks that require gathering information about and interacting with a partially observable environment, e.g. "Maintain localization error below a certain level while also avoiding obstacles.'' Third, we consider learning temporal logic properties of a dynamical system from a finite set of system outputs. For example, given maritime surveillance data we wish to find the specification that corresponds only to those vessels that are deemed law-abiding. Algorithms for performing off-line supervised and unsupervised learning and on-line supervised learning are presented. Finally, we consider the case in which we want to steer a system with unknown dynamics to satisfy a given temporal logic specification. We present a novel reinforcement learning paradigm to solve this problem. Our procedure gives "partial credit'' for executions that almost satisfy the specification, which can lead to faster convergence rates and produce better solutions when the specification is not satisfiable