Linear Temporal Logic-based Mission Planning

In this paper, we describe the Linear Temporal Logic-based reactive motion planning. We address the problem of motion planning for mobile robots, wherein the goal specification of planning is given in complex environments. The desired task specification may consist of complex behaviors of the robot, including specifications for environment constraints, need of task optimality, obstacle avoidance, rescue specifications, surveillance specifications, safety specifications, etc. We use Linear Temporal Logic to give a representation for such complex task specification and constraints. The specifications are used by a verification engine to judge the feasibility and suitability of plans. The planner gives a motion strategy as output. Finally a controller is used to generate the desired trajectory to achieve such a goal. The approach is tested using simulations on the LTLMoP mission planning tool, operating over the Robot Operating System. Simulation results generated using high level planners and low level controllers work simultaneously for mission planning and controlling the physical behavior of the robot

Barrier Functions for Multiagent-POMDPs with DTL Specifications

Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots

Optimal planning with temporal logic specifications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Includes bibliographical references (p. 117-121).Most of the current uninhabitated Aerial Vehicles (UAVs) are individually monitored, commanded and controlled by several operators of different expertise. However, looking forward, there has been a recent interest in multiple-UAV systems, in which the system is only provided with the high-level goals and constraints, called the "mission specifications," and asked to navigate the UAVs such that the mission specifications are fulfilled. A crucial part in designing such multiple-UAV systems is the development of coordination and planning algorithms that, given a set of high-level mission specifications as input, can synthesize provably correct and possibly optimal schedules for each of the UAVs. This thesis studies optimal planning problems in a multiple-UAV mission planning setting, where the mission specifications are given in formal languages. The problem is posed as a novel variant of the Vehicle Routing Problem (VRP), in which temporal logics and process algebra are utilized to represent a large class of mission specifications in a systematic way. The thesis is structured in two parts. In the first part, two temporal logics that are remarkably close to the natural language, namely the linear temporal logic LTL-x and the metric temporal logic (MTL), are considered for specification of a large class of temporal and logical constraints in VRPs. Mixed-integer linear programming based algorithms, which solve these variants of the VRP to optimality, are presented. In the second part, process algebra is introduced and used as a candidate for the same purpose.(cont.) A tree search based anytime algorithm is given; this algorithm is guarranteed to find a best-first feasible solution in polynomial time and improve it to an optimal one in finite time.by Sertac Karaman.S.M

Graph-Based Convexification of Nested Signal Temporal Logic Constraints for Trajectory Optimization

Optimizing high-level mission planning constraints is traditionally solved in exponential time and requires to split the problem into several ones, making the connections between them a convoluted task. This paper aims at generalizing recent works on the convexification of Signal Temporal Logic (STL) constraints converting them into linear approximations. Graphs are employed to build general linguistic semantics based on key words (such as Not, And, Or, Eventually, Always), and super-operators (e.g., Until, Implies, If and Only If) based on already defined ones. Numerical validations demonstrate the performance of the proposed approach on two practical use-cases of satellite optimal guidance using a modified Successive Convexification scheme

Control with Probabilistic Signal Temporal Logic

Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions.Comment: 7 pages, submitted to the 2016 American Control Conference (ACC 2016) on September, 30, 2015 (under review

Control with probabilistic signal temporal logic

Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions