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Maximum Allowable TCF Calibration Error for Robotic Pose Servoing

IEEE robotics AND automation LETTERS 2025年第2期10卷 1744-1751页

作者： Hou, Jun Xing, Shiyu Ma, Yunkai Jing, Fengshui Tan, Min Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Key Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China

Robotic pose servoing aims to move the robot end-effector to the target pose. Closed-loop servo systems can tolerate a small TCF (tool control frame) calibration error and accurately reach the target pose through multiple pose measurements and pose adjustments. However, the maximum allowable TCF calibration error remains an open question. This paper demonstrates that the necessary condition for robotic pose servoing is a TCF calibration error angle of less than 60 degrees, with no limit on the translational component of the TCF calibration error. Next, an improved pose servoing method is proposed to address the conflict between the large TCF error and the limited robot workspace. This method introduces a scaling factor to limit the adjustment range within the robot workspace, ensuring greater robustness. Finally, robot-assisted cabin docking is selected as an experimental validation case. Simulation and physical experiments validate the maximum allowable TCF calibration error. Comparative experiments confirm the robustness of the improved pose servoing method, achieving cabin docking despite significant TCF calibration errors.

关键词： Calibration Robots Robot kinematics Stars Assembly Servomotors Robustness Measurement uncertainty Accuracy Visualization formal methods in robotics and automation calibration and identification assembly

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Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities

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IEEE robotics AND automation LETTERS 2025年第2期10卷 2032-2039页

作者： Fang, Amy Yin, Tenny Kress-Gazit, Hadas Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14850 USA

Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities-either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL psi, an extension of LTL introduced in our prior work. We increase the expressivity of LTL psi by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.

关键词： Robots Collaboration Semantics Logic Runtime Mobile robots Planning Manipulators Grippers Grammar formal methods in robotics and automation task planning multi-robot systems

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A Signal Temporal Logic approach for task-based coordination of multi-aerial systems: A wind turbine inspection case study

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robotics AND AUTONOMOUS SYSTEMS 2025年 186卷

作者： Silano, Giuseppe Caballero, Alvaro Liuzza, Davide Iannelli, Luigi Bogdan, Stjepan Saska, Martin Czech Tech Univ Fac Elect Engn Dept Cybernet Prague Czech Republic Ric Sistema Energet RSE SpA Dept Power Generat Technocal & Mat Milan Italy Univ Seville GRVC Robot Lab Seville Spain Univ Sannio Benevento Dept Engn Benevento Italy Italian Natl Agcy New Technol Energy & Sustainable Fus & Technol Nucl Safety & Secur Dept Frascati Italy Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

The paper addresses task assignment and trajectory generation for collaborative inspection missions using a fleet of multi-rotors, focusing on the wind turbine inspection scenario. The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario.

关键词： Aerial systems Applications formal methods in robotics and automation Task and motion planning Multi-robot systems

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Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot Learning

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IEEE TRANSACTIONS ON robotics 2025年 41卷 926-945页

作者： Farid, Alec Veer, Sushant Pachisia, Divyanshu Majumdar, Anirudha Princeton Univ Princeton NJ 08540 USA Zoox Foster CA 94404 USA NVIDIA Autonomous Vehicle Res Grp Santa Clara CA 95051 USA Skydio San Mateo CA 94402 USA Princeton Univ Intelligent Robot Mot Lab Princeton NJ USA

Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on $p$-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false-positive and false-negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials.

关键词： Deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes Deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes

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Kinodynamic Motion Planning via Branch-and-Cut Over Probabilistic Roadmaps

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IEEE robotics AND automation LETTERS 2024年第1期9卷 247-254页

作者： Ramirez, Miquel Selvaratnam, Daniel Manzie, Chris Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3052 Australia KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst S-11428 Stockholm Sweden

This letter describes an extension of the classic Lazy Probabilistic Roadmaps algorithm (Lazy PRM), which results from pairing PRM and a novel Branch-and-Cut (BC) algorithm. Cuts are dynamically generated constraints that are imposed on minimum cost paths over the geometric graphs selected by PRM. Cuts eliminate paths that cannot be mapped into smooth plans that satisfy suitably defined geometric and differential constraints. We generate candidate smooth plans by fitting splines to vertices in a minimum-cost path. Plans are validated with a recently proposed algorithm that maps them into finite traces, without the need to choose a fixed discretization step. A trace records the exact sequence of constraint boundaries crossed by the plan, modulo arithmetic precision. We evaluate several planners using our methods over the recently proposed BARN benchmark, reporting evidence of the scalability of our approach.

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

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Determination of All Stable and Unstable Equilibria for Image-Point-Based Visual Servoing

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IEEE TRANSACTIONS ON robotics 2024年 40卷 3406-3424页

作者： Colotti, Alessandro Fontan, Jorge Garcia Goldsztejn, Alexandre Briot, Sebastien Chaumette, Francois Kermorgant, Olivier El Din, Mohab Safey Nantes Univ Ecole Cent Nantes CNRS F-44035 Nantes France Sorbonne Univ LIP6 F-75006 Paris France Univ Rennes Inria CNRS IRISA F-35000 Rennes France

Local minima are a well-known drawback of image-based visual servoing systems. Up to now, there were no formal guarantees on their number, or even their existence, according to the considered configuration. In this work, a formal approach is presented for the exhaustive computation of all minima and unstable equilibria for a class of six well-known image-based visual servoing controllers. This approach relies on a new polynomial formulation of the equilibrium condition that avoids using the camera pose. By using modern computational algebraic geometry methods and an ad hoc symmetry breaking strategy, the formal resolution of this new equilibrium condition is rendered computationally feasible. The proposed methodology is applied to compute the equilibria of several classical visual servoing tasks, with planar and nonplanar configurations of four and five points. The effects of local minima and saddle points on the dynamics of the system are finally illustrated through intensive simulation results, as well as the effects of image noise and uncertainties on depths.

关键词： Visualization Visual servoing Noise Computational modeling Robot kinematics Task analysis Polynomials formal methods in robotics and automation sensor-based control stability analysis visual servoing

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Safe POMDP Online Planning Among Dynamic Agents via Adaptive Conformal Prediction

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IEEE robotics AND automation LETTERS 2024年第11期9卷 9946-9953页

作者： Sheng, Shili Yu, Pian Parker, David Kwiatkowska, Marta Feng, Lu Univ Virginia Sch Engn & Appl Sci Charlottesville VA 22904 USA Univ Oxford Dept Comp Sci Oxford OX1 2JD England

Online planning for partially observable Markov decision processes (POMDPs) provides efficient techniques for robot decision-making under uncertainty. However, existing methods fall short of preventing safety violations in dynamic environments. This letter presents a novel safe POMDP online planning approach that maximizes expected returns while providing probabilistic safety guarantees amidst environments populated by multiple dynamic agents. Our approach utilizes data-driven trajectory prediction models of dynamic agents and applies Adaptive Conformal Prediction (ACP) to quantify the uncertainties in these predictions. Leveraging the obtained ACP-based trajectory predictions, our approach constructs safety shields on-the-fly to prevent unsafe actions within POMDP online planning. Through experimental evaluation in various dynamic environments using real-world pedestrian trajectory data, the proposed approach has been shown to effectively maintain probabilistic safety guarantees while accommodating up to hundreds of dynamic agents.

关键词： Safety Planning Robots Trajectory Uncertainty Pedestrians Heuristic algorithms formal methods in robotics and automation planning under uncertainty robot safety AI-enabled robotics

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Decomposition-Based Hierarchical Task Allocation and Planning for Multi-Robots Under Hierarchical Temporal Logic Specifications

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IEEE robotics AND automation LETTERS 2024年第8期9卷 7182-7189页

作者： Luo, Xusheng Xu, Shaojun Liu, Ruixuan Liu, Changliu Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Zhejiang Univ Coll Control Sci & Engn Hangzhou 310058 Peoples R China

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL (Luo et al., 2024) that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is used to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of navigation and manipulation. The simulation demonstrated that our approach can find better solutions using less runtimes.

关键词： Robots Task analysis Robot kinematics Logic Planning Resource management Navigation formal methods in robotics and automation multi-robot systems planning scheduling and coordination

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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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Rigid Body Path Planning Using Mixed-Integer Linear Programming

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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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