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A Framework for formal Verification of Behavior Trees With Linear Temporal Logic

IEEE robotics AND automation LETTERS 2020年第2期5卷 2341-2348页

作者： Biggar, Oliver Zamani, Mohammad Def Sci & Technol Grp Land Div Canberra NSW 3124 Australia

Despite the current increasing popularity of Behavior Trees (BTs) in the robotics community, there does not currently exist a method to formally verify their correctness without compromising their most valuable traits: modularity, flexibility and reusability. In this letter we present a new mathematical framework in which we formally express Behavior Trees in Linear Temporal Logic (LTL). We show how this framework equivalently represents classical BTs. Then we utilize the proposed framework to construct an algorithm to verify that a given BT satisfies a given LTL specification. We prove that this algorithm is sound. Importantly, we prove that this method does not compromise the flexible design process of BTs, i.e. changes to subtrees can be verified separately and their combination can be assured to be correct. We present an example of the proposed algorithm in use.

关键词： formal methods in robotics and automation control architectures and programming

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A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks With Multi-Robot Aerial Systems

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IEEE ACCESS 2023年 11卷 81361-81377页

作者： Caballero, Alvaro Silano, Giuseppe Univ Seville Dept Syst Engn & Automat Seville 41092 Spain Czech Tech Univ Dept Cybernet Prague 12135 Czech Republic

This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.

关键词： & nbsp Aerial systems applications formal methods in robotics and automation multi-robot systems task and motion planning

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Sampling-Based Reactive Synthesis for Nondeterministic Hybrid Systems

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IEEE robotics AND automation LETTERS 2024年第2期9卷 931-938页

作者： Ho, Qi Heng Sunberg, Zachary N. Lahijanian, Morteza Univ Colorado Dept Aerosp Engn Sci Boulder CO 80309 USA

This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy - a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.

关键词： formal methods in robotics and automation hybrid logical/dynamical planning and verification motion andpath planning planning under uncertainty

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Motion Planning Using Hyperproperties for Time Window Temporal Logic

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IEEE robotics AND automation LETTERS 2023年第8期8卷 4386-4393页

作者： Bonnah, Ernest Nguyen, Luan Hoque, Khaza Anuarul Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA Univ Dayton Dept Comp Sci Dayton OH 45469 USA

Hyperproperties are increasingly popular in verifying security policies and synthesis of control for dynamic systems. Hyperproperties generalize trace properties to enable reasoning about multiple computation traces that traditional trace properties cannot. Recent works show the effectiveness and prospect of Hyperproperties, specifically Hyperproperties for Linear Temporal Logic (HyperLTL), in optimality-, robustness-, and privacy-aware robotic motion planning. However, despite their rich expressiveness, HyperLTL cannot express tasks with time constraints. This letter presents HyperTWTL, which extends the compact semantics of Time Window Temporal Logic (TWTL) with explicit and concurrent quantification over multiple execution traces. We demonstrate that HyperTWTL can be used to formalize complex robotic planning objectives. Given HyperTWTL specifications, we also propose a symbolic approach for synthesizing optimality-, robustness-, and privacy-aware strategies by reducing the planning problem to a first-order logic satisfiability problem. The planning problem was then solved using two industrial-strength SMT solvers. The feasibility of HyperTWTL and the efficiency and scalability of the proposed strategy synthesis approach are demonstrated by formalizing important motion planning objectives of a surveillance mission case study and synthesizing the respective strategies using Z3 and CVC4 SMT solvers.

关键词： Planning Robots Semantics Service level agreements Task analysis Robustness Measurement formal methods in robotics and automation time window temporal logic hyperproperties motion planning

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Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled robotics and Autonomous Systems

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IEEE robotics AND automation LETTERS 2024年第4期9卷 3299-3306页

作者： Dong, Yi Zhao, Xingyu Wang, Sen Huang, Xiaowei Univ Liverpool Dept Comp Sci Liverpool L69 3BX Lancs England Univ Warwick WMG Coventry CV4 7AL Warwick England Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the presence of spuriously unsafe DRL policies. Unexplored states may lead the agent to make wrong decisions that could result in hazards, especially in applications where DRL-trained end-to-end controllers govern the behaviour of RAS. This letter proposes a novel quantitative reliability assessment framework for DRL-controlled RAS, leveraging verification evidence generated from formal reliability analysis of neural networks. A two-level verification framework is introduced to check the safety property with respect to inaccurate observations that are due to, e.g., environmental noise and state changes. Reachability verification tools are leveraged locally to generate safety evidence of trajectories. In contrast, at the global level, we quantify the overall reliability as an aggregated metric of local safety evidence, corresponding to a set of distinct tasks and their occurrence probabilities. The effectiveness of the proposed verification framework is demonstrated and validated via experiments on real RAS.

关键词： Safety Power system reliability Trajectory Reachability analysis Software reliability Robots Reinforcement learning Robot safety formal methods in robotics and automation AI-enabled robotics

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Event-Based Signal Temporal Logic Tasks: Execution and Feedback in Complex Environments

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IEEE robotics AND automation LETTERS 2022年第4期7卷 10001-10008页

作者： Gundana, David Kress-Gazit, Hadas Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

In this work, we synthesize control for high-level, reactive robot tasks that include timing constraints and choices over goals and constraints. We enrich Event-based Signal Temporal Logic by adding disjunctions, and propose a framework for synthesizing controllers that satisfy such specifications. If there are multiple ways to satisfy a specification, we choose, at run-time, a controller that instantaneously maximizes robustness. During execution, we automatically generate feedback in the form of pre-failure warnings that give users insight as to why a specification may be violated in the future. We demonstrate our work through physical and simulated multi-robot systems operating in complex environments.

关键词： formal methods in robotics and automation hybrid logical/dynamical planning and verification multi-robot systems

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Learning Linear Temporal Properties for Autonomous Robotic Systems

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IEEE robotics AND automation LETTERS 2023年第5期8卷 2930-2937页

作者： Ghiorzi, Enrico Colledanchise, Michele Piquet, Gianluca Bernagozzi, Stefano Tacchella, Armando Natale, Lorenzo Ist Italiano Tecnol I-40064 Genoa Italy Univ Genoa DIBRIS I-16100 Genoa Italy

The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized for execution speed. We apply it to the use-case of a robot moving in an unstructured environment as its battery discharges, both in simulation and in the real world. The results of our experiments confirm that our approach can learn temporal formulas explaining task failures in a case of practical interest.

关键词： Robots Task analysis Skeleton Syntactics Search problems Reactive power Cost accounting formal methods in robotics and automation autonomous agents failure detection and recovery

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Parameter Identifying Disturbance Rejection Control With Asymptotic Error Convergence

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IEEE robotics AND automation LETTERS 2024年第2期9卷 1035-1042页

作者： Patelski, Radoslaw Pazderski, Dariusz Poznan Univ Tech Inst Automat Control & Robot PL-60965 Poznan Poland

In this letter, a new kind of adaptive controller for the problem of output feedback tracking is proposed on the basis of the Active Disturbance Rejection Control (ADRC) paradigm. The controller is synthesized for the systems linear in parameters by combining the classic ADRC algorithm with a recent Parameter Identifying Extended State Observer (PIESO) which employs a gradient adaptation law to actively identify the parameters of the plant. By means of the Lyapunov analysis, the asymptotic convergence of tracking, estimation, and identification errors is proved in the nominal case and the stability conditions of the closed-loop system are formulated. The theoretical analysis is complemented by simulation and experimental results of the proposed controller.

关键词： Calibration and identification robust/adaptive control formal methods in robotics and automation

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On Null Space-Based Inverse Kinematics Techniques for Fleet Management: Toward Time-Varying Task Activation

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IEEE TRANSACTIONS ON robotics 2021年第1期37卷 257-274页

作者： Mannucci, Anna Caporale, Danilo Pallottino, Lucia Univ Pisa Res Ctr E Piaggio I-56126 Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Orebro Univ Multirobot Planning & Control Lab Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden

Multirobot fleets play an important role in industrial logistics, surveillance, and exploration applications. A wide literature exists on the topic, both resorting to reactive (i.e. collision avoidance) and to deliberative (i.e. motion planning) techniques. In this work, null space-based inverse kinematics (NSB-IK) methods are applied to the problem of fleet management. Several NSB-IK approaches existing in the literature are reviewed, and compared with a reverse priority approach, which originated in manipulator control, and is here applied for the first time to the considered problem. All NSB-IK approaches are here described in a unified formalism, which allows (i) to encode the property of each controller into a set of seven main key features, (ii) to study possible new control laws with an opportune choice of these parameters. Furthermore, motivated by the envisioned application scenario, we tackle the problem of task-switching activation. Leveraging on the iCAT TPC technique Simetti and Casalino, 2016, in this article, we propose a method to obtain continuity in the control in face of activation or deactivation of tasks, and subtasks by defining suitable damped projection operators. The proposed approaches are evaluated formally, and via simulations. Performances with respect to standard methods are compared considering a specific case study for multivehicles management.

关键词： Task analysis Kinematics Manipulators Robot kinematics Standards Trajectory formal methods in robotics and automation motion control multirobot systems path planning for multiple mobile robots or agents

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Optimal Stochastic Vehicle Path Planning Using Covariance Steering

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2276-2281页

作者： Okamoto, Kazuhide Tsiotras, Panagiotis Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

This letter addresses the problem of vehicle path planning in the presence of obstacles and uncertainties, a fundamental robotics problem. While several path planning algorithms have been proposed over the years, many of them have dealt with only deterministic environments orwith only open-loop uncertainty, i.e., the uncertainty of the system state is not controlled and, typically, increases with time because of exogenous disturbances. This may lead to potentially conservative nominal paths. The typical approach to deal with disturbances and reduce uncertainty is to use a lower level feedback controller. We advocate the premise that, if a path planner can consider the closed-loop evolution of the system uncertainty, it can lead to less conservative, but still feasible, paths. To this end, in this letter, we develop an approach that is based on optimal covariance steering, which explicitly steers the state covariance for stochastic linear systems. We verify the proposed framework using extensive numerical simulations.

关键词： Motion and path planning robust/adaptive control of robotic systems probability and statistical methods formal methods in robotics and automation

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