## SAMPLING-BASED REACTIVE MOTION PLANNING WITH TEMPORAL LOGIC CONSTRAINTS AND IMPERFECT STATE INFORMATION

FJ Montana, J Liu, TJ Dodd

Critical Systems: Formal Methods and Automated Verification (2017)

This paper presents a method that allows mobile systems with uncertainty in motion and sensing to react to unknown environments while high-level specifications are satisfied. Although previous works have addressed the problem of synthesising controllers under uncertainty constraints and temporal logic specifications, reaction to dynamic environments has not been considered under this scenario. The method uses feedback-based information roadmaps (FIRMs) to break the curse of history associated with partially observable systems. A transition system is incrementally constructed based on the idea of FIRMs by adding nodes on the belief space. Then, a policy is found in the product Markov decision process created between the transition system and a Rabin automaton representing a linear temporal logic formula. The proposed solution allows the system to react to previously unknown elements in the environment. To achieve fast reaction time, a FIRM considering the probability of violating the specification in each transition is used to drive the system towards local targets or to avoid obstacles. The method is demonstrated with an illustrative example.

## SAMPLING-BASED PATH PLANNING FOR MULTI-ROBOT SYSTEMS WITH CO-SAFE LINEAR TEMPORAL LOGIC SPECIFICATIONS

FJ Montana, J Liu, TJ Dodd

Critical Systems: Formal Methods and Automated Verification (2017)

This paper addresses the problem of path planning for multiple robots under high-level specifications given as syntactically co-safe linear temporal logic formulae. Most of the existing solutions use the notion of abstraction to obtain a discrete transition system that simulates the dynamics of the robot. Nevertheless, these solutions have poor scalability with the dimension of the configuration space of the robots. For problems with a single robot, sampling-based methods have been presented as a solution to alleviate this limitation. The proposed solution extends the idea of sampling methods to the multiple robot case. The method samples the configuration space of the robots to incrementally constructs a transition system that models the motion of all the robots as a group. This transition system is then combined with a Buchi automaton, representing the specification, in a Cartesian product. The product is updated with each expansion of the transition system until a solution is found. We also present a new algorithm that improves the performance of the proposed method by guiding the expansion of the transition system. The method is demonstrated with examples considering different number of robots and specifications.

## SAMPLING-BASED STOCHASTIC OPTIMAL CONTROL WITH METRIC INTERVAL TEMPORAL LOGIC SPECIFICATIONS

FJ Montana, J Liu, TJ Dodd

Conference on Control Applications (2016)

This paper describes a method to find optimal policies for stochastic dynamic systems that maximise the probability of satisfying real-time properties. The method consists of two phases. In the first phase, a coarse abstraction of the original system is created. In each region of the abstraction, a sampling-based algorithm is utilised to compute local policies that allow the system to move between regions. Then, in the second phase, the selection of a policy in each region is obtained by solving a reachability problem on the Cartesian product between the abstraction and a timed automaton representing a real-time specification given as a metric interval temporal logic formula. In contrast to current methods that require a fine abstraction, the proposed method achieves computational tractability by modelling the coarse abstraction of the system as a bounded-parameter Markov decision process (BMDP). Moreover, once the BMDP is created, this can be reused for new specifications assuming the same stochastic system and workspace. The method is demonstrated with an autonomous driving example.