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Rigid Body Path Planning Using Mixed-Integer Linear Programming

IEEE robotics AND automation LETTERS 2024年第11期9卷 10026-10033页

作者： Yu, Mingxin Fan, Chuchu MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

Navigating rigid body objects through crowded environments can be challenging, especially when narrow passages are presented. Existing sampling-based planners and optimization-based methods like mixed integer linear programming (MILP) formulations, suffer from limited scalability with respect to either the size of the workspace or the number of obstacles. In order to address the scalability issue, we propose a three-stage algorithm that first generates a graph of convex polytopes in the workspace free of collision, then poses a large set of small MILPs to generate viable paths between polytopes, and finally queries a pair of start and end configurations for a feasible path online. The graph of convex polytopes serves as a decomposition of the free workspace and the number of decision variables in each MILP is limited by restricting the subproblem within two or three free polytopes rather than the entire free region. Our simulation results demonstrate shorter online computation time compared to baseline methods and scales better with the size of the environment and tunnel width than sampling-based planners in both 2D and 3D environments.

关键词： Motion and path planning formal methods in robotics and automation Motion and path planning formal methods in robotics and automation

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Safe Networked robotics With Probabilistic Verification

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IEEE robotics AND automation LETTERS 2024年第3期9卷 2917-2924页

作者： Narasimhan, Sai Shankar Bhat, Sharachchandra Chinchali, Sandeep P. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation, or the cloud, that runs compute-intensive deep neural network perception or control models. However, this assistance comes at the cost of a time delay due to network latency, resulting in past observations being used in the cloud to compute the control commands for the present robot state. Such communication delays could potentially lead to the violation of essential safety properties, such as collision avoidance. This article develops methods to ensure the safety of robots operated over communication networks with stochastic latency. To do so, we use tools from formal verification to construct a shield, i.e., a run-time monitor, that provides a list of safe actions for any delayed sensory observation, given the expected and maximum network latency. Our shield is minimally intrusive and enables networked robots to satisfy key safety constraints, expressed as temporal logic specifications, with desired probability. We demonstrate our approach on a real F1/10th autonomous vehicle that navigates in indoor environments and transmits rich LiDAR sensory data over congested WiFi links.

关键词： formal methods in robotics and automation networked robots teleoperation probabilistic verification

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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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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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Cost-Aware Path Planning Under Co-Safe Temporal Logic Specifications

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IEEE robotics AND automation LETTERS 2017年第4期2卷 2308-2315页

作者： Cho, Kyunghoon Suh, Junghun Tomlin, Claire J. Oh, Songhwai Seoul Natl Univ Dept Elect & Comp Engn Seoul 151742 South Korea Seoul Natl Univ ASRI Seoul 151742 South Korea Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

This letter presents a path planning algorithm for generating a cost-efficient path that satisfies mission requirements specified in linear temporal logic (LTL). We assume that a cost function is defined over the configuration space. Examples of a cost function include hazard levels, wireless connectivity, and energy consumption, to name a few. The proposed method consists of two parts: sampling-based cost-aware path planning considering the vehicle dynamics based on rapidly-exploring random trees (RRT*), and a high-level logic which determines how to extend the RRT tree based on spatiotemporal specifications of an LTL formula. In order to find a low-cost trajectory with computational efficiency, the proposed method expands the RRT tree with long extensions using cross entropy, while the rewiring step of RRT* is used to preserve the asymptotic optimality. In simulation and experiments, we show that the proposed method performs favorably compared to existing methods.

关键词： formal methods in robotics and automation motion and path planning

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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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Probabilistic Coordination of Heterogeneous Teams From Capability Temporal Logic Specifications

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IEEE robotics AND automation LETTERS 2022年第2期7卷 1190-1197页

作者： Cai, Mingyu Leahy, Kevin Serlin, Zachary Vasile, Cristian-Ioan Lehigh Univ Mech Engn Bethlehem PA 18015 USA MIT Lincoln Lab Lexington MA 02421 USA

This letter explores coordination of heterogeneous teams of agents from high-level specifications. We employ Capability Temporal Logic (CaTL) to express rich, temporal-spatial tasks that require cooperation between many agents with unique capabilities. CaTL specifies combinations of tasks, each with desired locations, duration, and set of capabilities, freeing the user from considering specific agent trajectories and their impact on multi-agent cooperation. CaTL also provides a quantitative robustness metric of satisfaction based on availability of required capabilities for each task. The novelty of this letter focuses on satisfaction of CaTL formulas under probabilistic conditions. Specifically, we consider uncertainties in robot motion (e.g., agents may fail to transition between regions with some probability) and local probabilistic workspace properties (e.g., if there are not enough agents of a required capability to complete a collaborative task). The proposed approach automatically formulates a mixed-integer linear program given agents, their dynamics and capabilities, an abstraction of the workspace, and a CaTL formula. In addition to satisfying the given CaTL formula, the optimization considers the following secondary goals (in decreasing order of priority): 1) minimize the risk of transition failure due to uncertainties;2) maximize probabilities of regional collaborative satisfaction (if there is an excess of agents);3) maximize the availability robustness of CaTL for potential agent attrition;4) minimize the total agent travel time. We evaluate the performance of the proposed framework and demonstrate its scalability via numerical simulations.

关键词： formal methods in robotics and automation multi-robot systems planning scheduling and coordination

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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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Learning-Based Bayesian Inference for Testing of Autonomous Systems

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IEEE robotics AND automation LETTERS 2024年第10期9卷 9063-9070页

作者： Parashar, Anjali Yin, Ji Dawson, Charles Tsiotras, Panagiotis Fan, Chuchu MIT Lab Informat & Decis Syst LIDS Cambridge MA 02139 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

For the safe operation of robotic systems, it is important to accurately understand its failure modes using prior testing. Hardware testing of robotic infrastructure is known to be slow and costly. Instead, failure prediction in simulation can help to analyze the system before deployment. Conventionally, large-scale na & iuml;ve Monte Carlo simulations are used for testing;however, this method is only suitable for testing average system performance. For safety-critical systems, worst-case performance is more crucial as failures are often rare events, and the size of test batches increases substantially as failures become rarer. Rare-event sampling methods can be helpful;however, they exhibit slow convergence and cannot handle constraints. This research introduces a novel sampling-based testing framework for autonomous systems which bridges these gaps by utilizing a discretized gradient-based second-order Langevin algorithm combined with learning-based techniques for constrained sampling of failure modes. Our method can predict more diverse failures by exploring the search space efficiently and ensures feasibility with respect to temporal and implicit constraints. We demonstrate the use of our testing methodology on two categories of testing problems, via simulations and hardware experiments. Our method discovers up to 2X failures compared to na & iuml;ve Random Walk sampling, with only half of the sample size.

关键词： Testing Optimization Bayes methods Dynamical systems Convergence Space exploration Robots Robot safety probabilistic inference formal methods in robotics and automation

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Online Modifications for Event-Based Signal Temporal Logic Specifications

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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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