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Collision Tests in Human-Robot Collaboration: Experiments on the Influence of Additional Impact Parameters on Safety

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IEEE ACCESS 2023年 11卷 118395-118413页

作者： Fischer, Clara Neuhold, Michael Steiner, Martin Haspl, Thomas Rathmair, Michael Schlund, Sebastian TU Wien Inst Management Sci A-1040 Vienna Austria TUV Austria Serv GmbH A-1230 Vienna Austria Joanneum Res Robot A-9020 Klagenfurt Austria

Human-robot collaborations are among the most promising but challenging applications for human-centered production, as both agents involved share a mutual workspace without physical barriers. Safety aspects require limiting the power and force of operations to comply with biomechanical limits in case of a collision according to the International Organization for Standardization (ISO) technical specification ISO/TS 15066:2016. This analysis requires highly elaborate physical crash tests of potential impact scenarios. formal verification will save considerable amounts of time and effort. However, the equations presented in current guidelines need to be more accurate as many factors influencing the impact force and pressure are not considered, making these equations impractical for the evaluation of real applications. To answer the question of which parameters influence a human-robot collision, we have experimentally analyzed different collision scenarios in various configurations. The main results show the correlation of impactor geometries, edges versus rounding, and Shore hardness of the affected body region that simulates human tissue. This work presents the parameters that a formal human-robot collision verification model should contain, based on experimental tests. Furthermore, a model that identifies a collision's worst-case impacted body region is introduced. The results of this work will help in simplifying and accelerating the safety evaluation process of collaborative human-robot applications to better realize their potential.

关键词： Industrial robot safety human-robot collaboration (HRC) formal methods in robotics and automation power and force limitation robot collision evaluation

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Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis

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IEEE robotics AND automation LETTERS 2023年第5期8卷 2692-2699页

作者： Chakraborty, Kaustav Bansal, Somil Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA

Machine learning driven image-based controllers allow robotic systems to take intelligent actions based on the visual feedback from their environment. Understanding when these controllers might lead to system safety violations is important for their integration in safety-critical applications and engineering corrective safety measures for the system. Existing methods leverage simulation-based testing (or falsification) to find the failures of vision-based controllers, i.e., the visual inputs that lead to closed-loop safety violations. However, these techniques do not scale well to the scenarios involving high-dimensional and complex visual inputs, such as RGB images. In this work, we cast the problem of finding closed-loop vision failures as a Hamilton-Jacobi (HJ) reachability problem. Our approach blends simulation-based analysis with HJ reachability methods to compute an approximation of the backward reachable tube (BRT) of the system, i.e., the set of unsafe states for the system under vision-based controllers. Utilizing the BRT, we can tractably and systematically find the system states and corresponding visual inputs that lead to closed-loop failures. These visual inputs can be subsequently analyzed to find the input characteristics that might have caused the failure. Besides its scalability to high-dimensional visual inputs, an explicit computation of BRT allows the proposed approach to capture non-trivial system failures that are difficult to expose via random simulations. We demonstrate our framework on two case studies involving an RGB image-based neural network controller for (a) autonomous indoor navigation, and (b) autonomous aircraft taxiing.

关键词： Robots Visualization Safety Robot sensing systems Computational modeling Aircraft navigation Aircraft Robot Safety formal methods in robotics and automation optimization and optimal control vision-based navigation

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Learning Performance Graphs From Demonstrations via Task-Based Evaluations

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IEEE robotics AND automation LETTERS 2023年第1期8卷 336-343页

作者： Puranic, Aniruddh G. Deshmukh, Jyotirmoy V. Nikolaidis, Stefanos Univ Southern Calif Comp Sci Dept Los Angeles CA 90089 USA

In the paradigm of robot learning-from-demonstra tions (LfD), understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without this knowledge, a robot may infer incorrect reward functions that lead to undesirable or unsafe control policies. Prior work has used temporal logic specifications, manually ranked by human experts based on their importance, to learn reward functions from imperfect/suboptimal demonstrations. To overcome reliance on expert rankings, we propose a novel algorithm that learns from demonstrations, a partial ordering of provided specifications in the form of a performance graph. Through various experiments, including simulation of industrial mobile robots, we show that extracting reward functions with the learned graph results in robot policies similar to those generated with the manually specified orderings. We also show in a user study that the learned orderings match the orderings or rankings by participants for demonstrations in a simulated driving domain. These results show that we can accurately evaluate demonstrations with respect to provided task specifications from a small set of imperfect data with minimal expert input.

关键词： formal methods in robotics and automation learning from demonstration reinforcement learning

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The Kinematics of Constant Curvature Continuum Robots Through Three Segments

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IEEE robotics AND automation LETTERS 2023年第11期8卷 7631-7638页

作者： Li, Yucheng Myszka, David H. Murray, Andrew Univ Dayton Dept Mech & Aerosp Engn Design Innovat Machines Lab DIMLab Dayton OH 45469 USA

This letter investigates the mathematical relationships between the positions and orientations at the segment tips of a piecewise constant curvature (PCC) continuum robot with up to three segments. For one-segment, a reachability criterion is proposed, simplifying the calculation of the neighboring orientation. For two-segments, a reachability criterion is proposed and the redundancy of its inverse kinematics solution is found, establishing a circle of tip locations. For three-segments, the redundancy of the inverse kinematics includes tips that lie on a sphere providing a closed-form solution to the inverse kinematics problem. These relationships are derived from the unique characteristics of the bisecting plane of a single segment. The degenerate cases for the solutions are also addressed. These outcomes stem from a specific PCC parametrization, with implications extending to the general PCC model. Note that this study is grounded solely in simulation.

关键词： Kinematics formal methods in robotics and automation soft robot applications constant curvature continuum robots

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Model Predictive Robustness of Signal Temporal Logic Predicates

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IEEE robotics AND automation LETTERS 2023年第12期8卷 8050-8057页

作者： Lin, Yuanfei Li, Haoxuan Althoff, Matthias Tech Univ Munich Sch Computat Informat & Technol D-85748 Garching Germany

The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of predicates is usually defined in a model-free way, i.e., without including the system dynamics. Moreover, it is often nontrivial to define the robustness of complicated predicates precisely. To address these issues, we propose a notion of model predictive robustness, which provides a more systematic way of evaluating robustness compared to previous approaches by considering model-based predictions. In particular, we use Gaussian process regression to learn the robustness based on precomputed predictions so that robustness values can be efficiently computed online. We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of precision. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.

关键词： Robustness Predictive models Computational modeling Trajectory Safety Planning Autonomous vehicles formal methods in robotics and automation integrated planning and learning signal temporal logic model predictive robustness Gaussian process regression

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Closed Loop Static Control of Multi-Magnet Soft Continuum Robots

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IEEE robotics AND automation LETTERS 2023年第7期8卷 3980-3987页

作者： Pittiglio, Giovanni Orekhov, Andrew L. da Veiga, Tomas Calo, Simone Chandler, James H. Simaan, Nabil Valdastri, Pietro Harvard Med Sch Boston Childrens Hosp Dept Cardiovasc Surg Boston MA 02115 USA Univ Leeds Inst Autonomous Syst & Sensing IRASS Sch Elect & Elect Engn STORM Lab Leeds LS2 9JT England Vanderbilt Univ Dept Mech Engn Nashville TN 37235 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15289 USA

This letter discusses a novel static control approach applied to magnetic soft continuum robot (MSCR). Our aim is to demonstrate the control of a multi-magnet soft continuum robot (SCR) in 3D. The proposed controller, based on a simplified yet accurate model of the robot, has a high update rate and is capable of real-time shape control. For the actuation of the MSCR, we employ the dual external permanent magnet (dEPM) platform and we sense the shape via fiber Bragg grating (FBG). The employed actuation system and sensing technique makes the proposed approach directly applicable to the medical context. We demonstrate that the proposed controller, running at approximately 300 Hz, is capable of shape tracking with a mean error of 8.5% and maximum error of 35.2%. We experimentally show that the static controller is 25.9% more accurate than a standard PID controller in shape tracking and is able to reduce the maximum error by 59.2%.

关键词： Robots Robot sensing systems Computational modeling Shape Kinematics Sensors Three-dimensional displays Medical robots and systems formal methods in robotics and automation modeling control learning for soft robots magnetic actuation

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Collaborative Magnetic Manipulation via Two Robotically Actuated Permanent Magnets

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IEEE TRANSACTIONS ON robotics 2023年第2期39卷 1407-1418页

作者： Pittiglio, Giovanni Brockdorff, Michael da Veiga, Tomas Davy, Joshua Chandler, James Henry Valdastri, Pietro Harvard Med Sch Boston Childrens Hosp Dept Cardiovasc Surg Boston MA 02115 USA Univ Leeds Sch Elect & Elect Engn Inst Autonomous Syst & Sensing STORM Lab Leeds LS2 9JT W Yorkshire England

Magnetically actuated robots have proven effective in several applications, specifically in medicine. However, generating high actuating fields with a high degree of manipulability is still a challenge, especially when the application needs a large workspace to suitably cover a patient. The presented work discusses a novel approach for the control of magnetic field and field gradients using two robotically actuated permanent magnets. In this case, permanent magnets-relative to coil-based systems-have the advantage of larger field density without energy consumption. We demonstrate that collaborative manipulation of the two permanent magnets can introduce up to three additional Degrees of Freedom (DOFs) when compared to single permanent magnet approaches (five DOFs). We characterized the dual-arm system through the measurement of the fields and gradients and show accurate open-loop control with a 13.5% mean error. We then demonstrate how the magnetic DOFs can be employed in magnetomechanical manipulation, by controlling and measuring the wrench on two orthogonal magnets within the workspace, observing a maximum crosstalk of 6.1% and a mean error of 11.1%.

关键词： Magnetomechanical effects Magnetic separation Magnetic resonance imaging Coils Permanent magnets Sensors Robot sensing systems Force control formal methods in robotics and automation magnetic actuation medical robots and systems

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Physically Feasible Repair of Reactive, Linear Temporal Logic-Based, High-Level Tasks

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IEEE TRANSACTIONS ON robotics 2023年第6期39卷 4653-4670页

作者： Pacheck, Adam Kress-Gazit, Hadas Cornell Univ Ithaca Ithaca NY 14850 USA

A typical approach to creating complex robot behaviors is to compose atomic controllers, or skills, such that the resulting behavior satisfies a high-level task;however, when a task cannot be accomplished with a given set of skills, it is difficult to know how to modify the skills to make the task possible. We present a method for combining symbolic repair with physical feasibility checking and implementation to automatically modify existing skills such that the robot can execute a previously infeasible task. We encode robot skills in linear temporal logic (LTL) formulas that capture both safety constraints and goals for reactive tasks. Furthermore, our encoding captures the full skill execution, as opposed to prior work where only the state of the world before and after the skill is executed are considered. Our repair algorithm suggests symbolic modifications, then attempts to physically implement the suggestions by modifying the original skills subject to Linear Temporal Logic (LTL) constraints derived from the symbolic repair. If skills are not physically possible, we automatically provide additional constraints for the symbolic repair. We demonstrate our approach with a Baxter and a Clearpath Jackal.

关键词： Failure detection and recovery formal methods in robotics and automation specification repair task planning

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Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods for robotics and Control

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IEEE TRANSACTIONS ON robotics 2023年第3期39卷 1749-1767页

作者： Dawson, Charles Gao, Sicun Fan, Chuchu MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced transparency and lack of guarantees on the safety or stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies-these certificates provide concise data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this article, we provide a comprehensive survey of this rapidly developing field of certificate learning. We hope that this article will serve as an accessible introduction to the theory and practice of certificate learning, both to those who wish to apply these tools to practical robotics problems and to those who wish to dive more deeply into the theory of learning for control.

关键词： Lyapunov methods Safety Robots Asymptotic stability Trajectory Measurement Trajectory tracking Deep learning in robotics and automation formal methods in robotics and automation neural certificates robot safety

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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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