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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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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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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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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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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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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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Resilient Online Planning for Mobile Robots With Minimal Relaxation of Signal Temporal Logic Specifications

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IEEE robotics AND automation LETTERS 2025年第6期10卷 5935-5942页

作者： Buyukkocak, Ali Tevfik Aksaray, Derya Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

We address the problem of resilient motion planning for robots operating under Signal Temporal Logic (STL) specifications in dynamic environments. In such settings, unforeseen events-such as the emergence of dynamic obstacles-can render the original STL specification infeasible. To address this, we propose a reactive framework that enables local corrections or global replanning of the robot's trajectory in response to these unexpected occurrences. When the original STL specification becomes unsatisfiable, our framework involves minimally relaxing it by extending/shrinking time windows or removing some tasks within the user's allowance. This strategy is designed to prevent arbitrarily long delays in mission completion and to facilitate the satisfaction of the mission with minimal temporal relaxation (TR). We present theoretical results supporting our framework and demonstrate its effectiveness through high-fidelity simulations.

关键词： formal methods in robotics and automation motion and path planning reactive and sensor-based planning formal methods in robotics and automation motion and path planning reactive and sensor-based planning

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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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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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