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Online Modifications for Event-Based Signal Temporal Logic Specifications

IEEE robotics AND automation LETTERS 2024年第8期9卷 6864-6871页

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

In this letter we present a grammar and control synthesis framework for online modification of Event-based Signal Temporal Logic (STL) specifications, during execution. These modifications allow a user to change the robots' task in response to potential future violations, changes to the environment, or user-defined task changes. In cases where a modification is not possible, we provide feedback to the user and suggest alternative modifications. We demonstrate our task modification process using a Hello Robot Stretch.

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

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Joint Stress Estimation and Remaining Useful Life Prediction for Collaborative Robots to Support Predictive Maintenance

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IEEE robotics AND automation LETTERS 2024年第4期9卷 3554-3561页

作者： Kolvig-Raun, Emil Stubbe Kjaergaard, Mikkel Baun Brorsen, Ralph Univ Southern Denmark SDU Software Engn Odense DK-5000 Denmark Universal Robots DK-5260 Odense Denmark

Anticipating the maintenance needs of lightweight robotic manipulators at precise future instances represents a significant challenge within the automation domain. This letter introduces an innovative and comprehensive method to estimate the severity of stress imposed on a robot joint at any given time. Additionally, we present a knowledge-based predictive model aimed at approximating the End of Life (EoL) for a robotic joint, enabling the prediction of its Remaining Useful Life (RUL) with respect to the designated load case. This predictive model is rooted in a baseline derived from empirical data covering the entire Universal Robots (UR) e-series and is trained using synthetic data. Subsequently, it undergoes evaluation with a real-world dataset and is further validated in a case study. The model demonstrates a high level of accuracy, with worst-case performance reaching 90,3 % as the lower limit.

关键词： Robots Stress Mathematical models Service robots Maintenance engineering Torque Temperature measurement Collaborative robots in manufacturing formal methods in robotics and automation industrial automation robot manipulators predictive maintenance

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A Learning-Based Method for Computing Control Barrier Functions of Nonlinear Systems With Control Constraints

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IEEE robotics AND automation LETTERS 2023年第7期8卷 4259-4266页

作者： Zhang, Ziyou Zhao, Qianchuan Sun, Kailai Tsinghua Univ Ctr Intelligent & Networked Syst CFINS Dept Automat & BNRist Beijing 100084 Peoples R China

Verifying the safety of states and designing a safety controller are very important for safety critical systems (for example, robotic and automotive systems). Since the reachable set of a state is hard to calculate online, it is difficult to determine whether the current state will enter the unsafe set in the future. Thus, the safe set of a system is usually obtained by calculating the forward invariant set offline. For control affine systems, the control barrier function provides a linear safety constraint on the control, which helps us obtain safety control online. However, the computation of control barrier functions is recognized as difficult, and existing methods make over-approximations or are only applicable to problems without control constraints. In this letter, we propose a learning-based method to compute the control barrier function for nonlinear control affine systems with control constraints. The method consists of a constraint satisfaction (CS) algorithm and a set expansion (SE) algorithm. The CS algorithm computes the control barrier function that satisfies the constraints, and the SE algorithm expands the volume of the safe set. The numerical results demonstrate that our algorithm outperforms existing methods and can improve existing control barrier functions.

关键词： AI-Based methods formal methods in robotics and automation machine learning for robot control

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Hand-Eye Calibration With a Remote Centre of Motion

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3121-3128页

作者： Pachtrachai, Krittin Vasconcelos, Francisco Dwyer, George Hailes, Stephen Stoyanov, Danail UCL Wellcome EPSRC Ctr Intervent & Surg Sci Dept Comp Sci London WC1E 6BT England UCL Dept Comp Sci London WC1E 6BT England

In the eye-in-hand robot configuration, hand-eye calibration plays a vital role in completing the link between the robot and camera coordinate systems. Calibration algorithms are mature and provide accurate transformation estimations for an effective camera-robot link but rely on a sufficiently wide range of calibration data to avoid errors and degenerate configurations. This can be difficult in the context of keyhole surgical robots because they are mechanically constrained to move around a remote centre of motion (RCM) which is located at the trocar port. The trocar limits the range of feasible calibration poses that can be obtained and results in ill-conditioned hand-eye constraints. In this letter, we propose a new approach to deal with this problem by incorporating the RCM constraints into the hand-eye formulation. We show that this not only avoids ill-conditioned constraints but is also more accurate than classic hand-eye calibration with a free 6DoF motion, due to solving simpler equations that take advantage of the reduced DoF. We validate our method using simulation to test numerical stability and a physical implementation on an RCM constrained KUKA LBR iiwa 14 R820 equipped with a NanEye stereo camera.

关键词： Calibration and identification formal methods in robotics and automation computer vision for medical robotics

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Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

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IEEE robotics AND automation LETTERS 2021年第4期6卷 6250-6257页

作者： Puranic, Aniruddh Gopinath V. Deshmukh, Jyotirmoy Nikolaidis, Stefanos Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA

Learning control policies that are safe, robust and interpretable are prominent challenges in developing robotic systems. Learning-from-demonstrations with formal logic is an arising paradigm in reinforcement learning to estimate rewards and extract robot control policies that seek to overcome these challenges. In this approach, we assume that mission-level specifications for the robotic system are expressed in a suitable temporal logic such as Signal Temporal Logic (STL). The main idea is to automatically infer rewards from user demonstrations (that could be suboptimal or incomplete) by evaluating and ranking them w.r.t. the given STL specifications. In contrast to existing work that focuses on deterministic environments and discrete state spaces, in this letter, we propose significant extensions that tackle stochastic environments and continuous state spaces.

关键词： Learning from demonstration probabilistic inference formal methods in robotics and automation

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Certified polyhedral decompositions of collision-free configuration space

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2024年第9期43卷 1322-1341页

作者： Dai, Hongkai Amice, Alexandre Werner, Peter Zhang, Annan Tedrake, Russ Toyota Res Inst Los Altos CA USA Massachusetts Inst Technol MIT Cambridge MA USA Toyota Res Inst 4440 El Camino Real Los Altos CA 94022 USA

Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for collisions at a point using standard algorithms, to date no practical method exists for computing C-free regions with rigorous certificates due to the complexity of mapping Cartesian-space obstacles through the kinematics. In this work, we present the first to our knowledge rigorous method for approximately decomposing a rational parametrization of C-free into certified polyhedral regions. Our method, called C-Iris (C-space Iterative Regional Inflation by Semidefinite programming), generates large, convex polytopes in a rational parameterization of the configuration space which are rigorously certified to be collision-free. Such regions have been shown to be useful for both optimization-based and randomized motion planning. Based on convex optimization, our method works in arbitrary dimensions, only makes assumptions about the convexity of the obstacles in the 3D Cartesian space, and is fast enough to scale to realistic problems in manipulation. We demonstrate our algorithm's ability to fill a non-trivial amount of collision-free C-space in several 2-DOF examples where the C-space can be visualized, as well as the scalability of our algorithm on a 7-DOF KUKA iiwa, a 6-DOF UR3e, and 12-DOF bimanual manipulators. An implementation of our algorithm is open-sourced in Drake. We furthermore provide examples of our algorithm in interactive Python notebooks.

关键词： Manipulation planning manipulation and grasping collision avoidance planning and simulation kinematics theoretical foundations formal methods in robotics and automation

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On Provably Safe and Live Multirobot Coordination With Online Goal Posting

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IEEE TRANSACTIONS ON robotics 2021年第6期37卷 1973-1991页

作者： Mannucci, Anna Pallottino, Lucia Pecora, Federico Univ Pisa Res Ctr E Piaggio Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Orebro Univ Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden

A standing challenge in multirobot systems is to realize safe and efficient motion planning and coordination methods that are capable of accounting for uncertainties and contingencies. The challenge is rendered harder by the fact that robots may be heterogeneous and that their plans may be posted asynchronously. Most existing approaches require constraints on the infrastructure or unrealistic assumptions on robot models. In this article, we propose a centralized, loosely-coupled supervisory controller that overcomes these limitations. The approach responds to newly posed constraints and uncertainties during trajectory execution, ensuring at all times that planned robot trajectories remain kinodynamically feasible, that the fleet is in a safe state, and that there are no deadlocks or livelocks. This is achieved without the need for hand-coded rules, fixed robot priorities, or environment modification. We formally state all relevant properties of robot behavior in the most general terms possible, without assuming particular robot models or environments, and provide both formal and empirical proof that the proposed fleet control algorithms guarantee safety and liveness.

关键词： Robots Robot kinematics Trajectory Collision avoidance System recovery Safety Task analysis formal methods in robotics and automation intelligent and flexible manufacturing multirobot systems planning scheduling and coordination

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Optimal Probabilistic Motion Planning With Potential Infeasible LTL Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 301-316页

作者： Cai, Mingyu Xiao, Shaoping Li, Zhijun Kan, Zhen Lehigh Univ Dept Mech Engn Bethlehem PA 18015 USA Univ Iowa Univ Iowa Technol Inst Dept Mech Engn Iowa City IA 52246 USA Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

This paper studies optimal motion planning subject to motion and environment uncertainties. By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the control objective is to synthesize a finite-memory policy, under which the agent satisfies complex high-level tasks expressed as linear temporal logic (LTL) with desired satisfaction probability. In particular, the cost optimization of the trajectory that satisfies infinite horizon tasks is considered, and the trade-off between reducing the expected mean cost and maximizing the probability of task satisfaction is analyzed. The LTL formulas are converted to limit-deterministic Buchi automata (LDBA) with a reachability acceptance condition and a compact graph structure. The novelty of this work lies in considering the cases where LTL specifications can be potentially infeasible and developing a relaxed product MDP between PL- MDP and LDBA. The relaxed product MDP allows the agent to revise its motion plan whenever the task is not fully feasible and quantify the revised plan's violation measurement. A multi- objective optimization problem is then formulated to jointly consider the probability of task satisfaction, the violation with respect to original task constraints, and the implementation cost of the policy execution. The formulated problem can be solved via coupled linear programs. This work first bridges the gap between probabilistic planning revision of potential infeasible LTL specifications and optimal control synthesis of both plan prefix and plan suffix of the trajectory over the infinite horizons. Experimental results are provided to demonstrate the effectiveness of the proposed framework.

关键词： Decision-making formal methods in robotics and automation linear programming (LP) motion planning network flow optimal control probabilistic model checking

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Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments From Temporal Logic Specifications

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IEEE robotics AND automation LETTERS 2023年第4期8卷 2158-2165页

作者： Cai, Mingyu Aasi, Erfan Belta, Calin Vasile, Cristian-Ioan Lehigh Univ Mech Engn Bethlehem PA 18015 USA Boston Univ Mech Engn Dept Boston MA 02215 USA

Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting in exploration issues during training. Such a problem becomes even more serious when tasks are described using temporal logic specifications. This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula. To overcome the environmental challenge of exploration during training, we propose a novel path planning-guided reward scheme by integrating sampling-based methods to effectively complete goal-reaching missions. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-goal-reaching tasks that are solved in a distributed manner. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale cluttered environments.

关键词： formal methods in robotics and automation deep reinforcement learning sampling-based method

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Coloured Petri Nets for Temporal Performance Evaluation of Distributed Control Systems-Application to a FIFO Queue

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IEEE robotics AND automation LETTERS 2022年第4期7卷 11268-11274页

作者： Bal Dit Sollier, Louis Ourghanlian, Alain Amari, Said EDF R&D PRISME F-78400 Chatou France ENS Paris Saclay LURPA F-91190 Gif Sur Yvette France

Temporal performances (like response time) of distributed control systems must he measurable for critical systems. The delay and reliability requirements require us to develop an exhaustive method to evaluate these performances through the most accurate models, such as (max,+) algebra combined with timed event graphs. Among the common mechanisms encountered in a distributed control system, the FIFO policy remains one of the mast used and cannot be modeled with timed event graphs. We show that the formalism of timed coloured Petri nets described in the (max,+) algebra can model the FIFO queue of a Programmable Logic Controller communication module. We also aim to reveal some exciting features of coloured Petri nets when analytically evaluating temporal performances. We present, therefore, a Timed Coloured Petri net modeling the emptying of a FIFO queue with a periodic task, and the formulas of delays associated with the induced system of (max, +) equations. We also test the consistency of our hounds in a numerical application of our model with positive results.

关键词： Discrete event dynamic automation systems distributed control systems formal methods in robotics and automation Petri nets for automation control response time evaluation

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