9,198 research outputs found

    Formal methods paradigms for estimation and machine learning in dynamical systems

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    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

    Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation

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    We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems. DTL can express properties involving uncertainty and likelihood that cannot be described by existing logics. A co-safe formulation of DTL is defined and algorithmic procedures are given for monitoring executions of a partially observable Markov decision process with respect to such formulae. A simulation case study of a rescue robotics application outlines our approach.Comment: More expanded version of "Distribution Temporal Logic: Combining Correctness with Quality of Estimation" to appear in IEEE CDC 201

    Multi-agent persistent surveillance under temporal logic constraints

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    This thesis proposes algorithms for the deployment of multiple autonomous agents for persistent surveillance missions requiring repeated, periodic visits to regions of interest. Such problems arise in a variety of domains, such as monitoring ocean conditions like temperature and algae content, performing crowd security during public events, tracking wildlife in remote or dangerous areas, or watching traffic patterns and road conditions. Using robots for surveillance is an attractive solution to scenarios in which fixed sensors are not sufficient to maintain situational awareness. Multi-agent solutions are particularly promising, because they allow for improved spatial and temporal resolution of sensor information. In this work, we consider persistent monitoring by teams of agents that are tasked with satisfying missions specified using temporal logic formulas. Such formulas allow rich, complex tasks to be specified, such as "visit regions A and B infinitely often, and if region C is visited then go to region D, and always avoid obstacles." The agents must determine how to satisfy such missions according to fuel, communication, and other constraints. Such problems are inherently difficult due to the typically infinite horizon, state space explosion from planning for multiple agents, communication constraints, and other issues. Therefore, computing an optimal solution to these problems is often infeasible. Instead, a balance must be struck between computational complexity and optimality. This thesis describes solution methods for two main classes of multi-agent persistent surveillance problems. First, it considers the class of problems in which persistent surveillance goals are captured entirely by TL constraints. Such problems require agents to repeatedly visit a set of surveillance regions in order to satisfy their mission. We present results for agents solving such missions with charging constraints, with noisy observations, and in the presence of adversaries. The second class of problems include an additional optimality criterion, such as minimizing uncertainty about the location of a target or maximizing sensor information among the team of agents. We present solution methods and results for such missions with a variety of optimality criteria based on information metrics. For both classes of problems, the proposed algorithms are implemented and evaluated via simulation, experiments with robots in a motion capture environment, or both

    Asymptotically Optimal Sampling-Based Motion Planning Methods

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    Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews, https://www.annualreviews.org/. 25 pages. 2 figure

    Provably-Correct Task Planning for Autonomous Outdoor Robots

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    Autonomous outdoor robots should be able to accomplish complex tasks safely and reliably while considering constraints that arise from both the environment and the physical platform. Such tasks extend basic navigation capabilities to specify a sequence of events over time. For example, an autonomous aerial vehicle can be given a surveillance task with contingency plans while complying with rules in regulated airspace, or an autonomous ground robot may need to guarantee a given probability of success while searching for the quickest way to complete the mission. A promising approach for the automatic synthesis of trusted controllers for complex tasks is to employ techniques from formal methods. In formal methods, tasks are formally specified symbolically with temporal logic. The robot then synthesises a controller automatically to execute trusted behaviour that guarantees the satisfaction of specified tasks and regulations. However, a difficulty arises from the lack of expressivity, which means the constraints affecting outdoor robots cannot be specified naturally with temporal logic. The goal of this thesis is to extend the capabilities of formal methods to express the constraints that arise from outdoor applications and synthesise provably-correct controllers with trusted behaviours over time. This thesis focuses on two important types of constraints, resource and safety constraints, and presents three novel algorithms that express tasks with these constraints and synthesise controllers that satisfy the specification. Firstly, this thesis proposes an extension to probabilistic computation tree logic (PCTL) called resource threshold PCTL (RT-PCTL) that naturally defines the mission specification with continuous resource threshold constraints; furthermore, it synthesises an optimal control policy with respect to the probability of success. With RT-PCTL, a state with accumulated resource out of the specified bound is considered to be failed or saturated depending on the specification. The requirements on resource bounds are naturally encoded in the symbolic specification, followed by the automatic synthesis of an optimal controller with respect to the probability of success. Secondly, the thesis proposes an online algorithm called greedy Buchi algorithm (GBA) that reduces the synthesis problem size to avoid the scalability problem. A framework is then presented with realistic control dynamics and physical assumptions in the environment such as wind estimation and fuel constraints. The time and space complexity for the framework is polynomial in the size of the system state, which is efficient for online synthesis. Lastly, the thesis proposes a synthesis algorithm for an optimal controller with respect to completion time given the minimum safety constraints. The algorithm naturally balances between completion time and safety. This work proves an analytical relationship between the probability of success and the conditional completion time given the mission specification. The theoretical contributions in this thesis are validated through realistic simulation examples. This thesis identifies and solves two core problems that contribute to the overall vision of developing a theoretical basis for trusted behaviour in outdoor robots. These contributions serve as a foundation for further research in multi-constrained task planning where a number of different constraints are considered simultaneously within a single framework

    Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

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    The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed
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