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4th International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Abdelghafar, Sara Darwish, Ashraf Hassanien, Aboul Ella Al Azhar Univ Fac Sci Comp Sci Dept Cairo Egypt Helwan Univ Fac Sci Cairo Egypt Cairo Univ Fac Comp & Informat IT Dept Giza Egypt SRGE Giza Egypt

ISBN: (纸本)9783319990101;9783319990095

failure detection and recovery is one of the important operations of the health monitoring management system which plays a relevant role for keeping the reliability and availability of the failed sensor over an on-orbit system whole lifetime mission especially where the maintenance may be impossible. In this paper we have implemented Grey Wolf Optimization (GWO) for optimizing Support Vector Machines (SVM) in terms of recovering the detected failure of the failed sensor. The performance of the proposed model with GWO is compared against to four swarm algorithms;Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA), Moth Flame Optimizer (MFO) and Whale Optimizer Algorithm (WOA), four different evaluation aspects are used in this comparison;failure recovering accuracy, stability, convergence and computational time. The experiment is implemented using cube satellite telemetry data, the experimental results demonstrate that the optimization of SVM using GWO (SVM-GWO) model can be regarded as a promising success for satellite failure detection and recovery.

关键词： failure detection and recovery Grey Wolf Optimization Telemetry data mining Support Vector Machine Satellite health monitoring

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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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Slip detection and recovery for Quadruped Robots via Orthogonal Decomposition

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第6期72卷 6166-6174页

作者： Yan, Chengzhen Qin, Jiahu Liu, Qingchen Ma, Qichao Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China

Locomotion on surfaces with insufficient friction poses a significant challenge for quadruped robots. Regular legged control methods derive contact forces by assuming stationary foot contact, which is no longer valid under slippery conditions. This article addresses this issue by designing a systematic antislip approach with a proprioceptive slip detector and a hybrid position/force slip recovery controller. The proposed slip detector estimates the likelihood of slippage occurrence without any external sensors. Meanwhile, the slip recovery controller extends the existing stationary contact constraint to the slippery case, which orthogonally decomposes the task of contact force control and slip recovery position control by projecting them into different subspaces. We also demonstrate that the proposed controller can be utilized to measure the friction coefficient of the contact surface for online parameter tuning. The proposed method is released as an open-source program that can be easily integrated into other model-based quadruped control frameworks and is experimentally validated on a Unitree A1 quadruped robot.

关键词： Robots Foot Quadrupedal robots Legged locomotion Sensors Detectors Robot kinematics Friction Force Robot sensing systems failure detection and recovery legged robots multicontact whole-body motion control

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Range-Based Multi-Robot Integrity Monitoring For Cyberattacks and Faults: An Anchor-Free Approach

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2630-2637页

作者： Vijay, Vishnu Pant, Kartik A. Cho, Minhyun Guo, Yifan Goppert, James M. Hwang, Inseok Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA

Coordination of multi-robot systems (MRSs) relies on efficient sensing and reliable communication among the robots. However, the sensors and communication channels of these robots are often vulnerable to cyberattacks and faults, which can disrupt their individual behavior and the overall objective of the MRS. In this work, we present a multi-robot integrity monitoring framework that utilizes inter-robot range measurements to (i) detect the presence of cyberattacks or faults affecting the MRS, (ii) identify the affected robot(s), and (iii) reconstruct the resulting localization error of these robot(s). The proposed iterative algorithm leverages sequential convex programming and alternating direction of multipliers method to enable real-time and distributed implementation. Our approach is validated using numerical simulations and demonstrated using PX4-SiTL in Gazebo on an MRS, where certain agents deviate from their desired position due to a GNSS spoofing attack. Furthermore, we demonstrate the scalability and interoperability of our algorithm through mixed-reality experiments by forming a heterogeneous MRS comprising real Crazyflie UAVs and virtual PX4-SiTL UAVs working in tandem.

关键词： Virtual reality Robot sensing systems Robot kinematics Fault detection Computer crime Network topology Sensors Noise Fault diagnosis Measurement uncertainty Multi-robot systems (MRSs) failure detection and recovery networked robots

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FR-Net: Learning Robust Quadrupedal Fall recovery on Challenging Terrains through Mass-Contact Prediction

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第7期10卷 6632-6639页

作者： Lu, Yidan Dong, Yinzhao Zhang, Jiahui Ma, Ji Lu, Peng Univ Hong Kong Dept Mech Engn Adapt Robot Controls Lab ArcLab Hong Kong 999077 Peoples R China

Fall recovery for legged robots remains challenging, particularly on complex terrains where traditional controllers fail due to incomplete terrain perception and uncertain interactions. We present FR-Net, a learning-based framework that enables quadrupedal robots to recover from arbitrary fall poses across diverse environments. Central to our approach is a Mass-Contact Predictor network that estimates the robot's mass distribution and contact states from limited sensory inputs, facilitating effective recovery strategies. Our carefully designed reward functions ensure safe recovery even on steep stairs without dangerous rolling motions common to existing methods. Trained entirely in simulation using privileged learning, our framework guides policy learning without requiring explicit terrain data during deployment. We demonstrate the generalization capabilities of FR-Net across different quadrupedal platforms in simulation and validate its performance through extensive real-world experiments on the Go2 robot in 10 challenging scenarios. Our results indicate that explicit mass-contact prediction is key to robust fall recovery, offering a promising direction for generalizable quadrupedal skills.

关键词： Robots Quadrupedal robots Training Legged locomotion Estimation Stairs Loss measurement History Heuristic algorithms Gaussian distribution Reinforcement learning legged robots failure detection and recovery

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The voraus-AD Dataset for Anomaly detection in Robot Applications

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 438-451页

作者： Brockmann, Jan Thies Rudolph, Marco Rosenhahn, Bodo Wandt, Bastian Voraus Robot GmbH D-30167 Hannover Germany Leibniz Univ Hannover Inst Informat Proc L3S D-30167 Hannover Germany Linkoping Univ Comp Vis Lab Linkoping Sweden

During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially occurring errors is included as unforeseeable events may happen over time. Therefore, anomaly detection (AD) delivers a practical solution, using only normal data to learn to detect unusual events. We introduce a dataset that allows training and benchmarking of anomaly detection methods for robotic applications based on machine data which will be made publicly available to the research community. As a typical robot task the dataset includes a pick-and-place application which involves movement, actions of the end effector, and interactions with the objects of the environment. Since several of the contained anomalies are not task-specific but general, evaluations on our dataset are transferable to other robotics applications as well. In addition, we present multivariate time-series flow (MVT-Flow) as a new baseline method for anomaly detection: It relies on deep-learning-based density estimation with normalizing flows, tailored to the data domain by taking its structure into account for the architecture. Our evaluation shows that MVT-Flow outperforms baselines from previous work by a large margin of 6.2% in area under receiving operator characteristic.

关键词： Dataset for anomaly detection (AD) deep learning in robotics and automation failure detection and recovery probability and statistical models

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Subsystem-Based Fault detection in Robotics via L2 Norm and Random Forest Models

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IEEE ACCESS 2024年 12卷 167613-167637页

作者： Alamoudi, Elham Abdullah Univ Aden Elect Engn Dept Aden Yemen Univ Engn & Technol Taxila Dept Elect Engn Taxila 47050 Pakistan

Robotic systems require effective fault detection, achievable through various methods, including data-driven techniques. This paper proposes a novel fault detection method that leverages the L2 norm of sensor measurements across different subsystems. Our approach involves dividing the system into subsystems, processing signals from a single sensor type, and using optimal features derived from the L2 norm for prediction via a random forest model for detection. This single-signal approach allows the algorithm to accurately classify data without being distracted by different types of signals, in addition to eliminating the curse of dimensionality. The approach was validated on the Robot Execution failures and Voraus-AD datasets, achieving exceptional performance across various metrics while significantly reducing data storage requirements, demonstrating both cost-effectiveness and computational efficiency.

关键词： failure detection and recovery dataset for fault detection machine learning in robotics and automation subsystem-based monitoring L2 norm for dimensionality reduction

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Multimodal detection and Classification of Robot Manipulation failures

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1396-1403页

作者： Inceoglu, Arda Aksoy, Eren Erdal Sariel, Sanem Istanbul Tech Univ Fac Comp & Informat Engn Artificial Intelligence & Robot Lab Turkiye TR-34718 Maslak Turkiye Halmstad Univ Ctr Appl Intelligent Syst Res Sch Informat Technol Halmstad Sweden

An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of the physical robot-object interaction may lead to failures in object manipulation. In this letter, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net (Inceoglu et al., 2021), a deep multimodal sensor fusion-based classifier network architecture. FINO-Net accurately detects and classifies failures from raw sensory data without any additional information on task description and scene state. In this work, we use our extended failure dataset (Inceoglu et al., 2021) with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that FINO-Net is also appropriate for real-time use.

关键词： Robot sensing systems Robots Task analysis Monitoring Hidden Markov models Collision avoidance Real-time systems Deep learning methods data sets for robot learning failure detection and recovery sensor fusion

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On the Calibration, Fault detection and recovery of a Force Sensing Device

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 9271-9278页

作者： Zhang, Yifang Ajoudani, Arash Tsagarakis, Nikos G. Ist Italiano Tecnol IIT Humanoids & Human Ctr Mechatron HHCM I-16163 Genoa Italy Ist Italiano Tecnol IIT Human Robot Interfaces & Interact HRI2 I-16163 Genoa Italy

Ground reaction force information, which includes the location of the center of pressure (COP) and vertical ground reaction force (vGRF), has various applications, such as in the gait assessment of patients post-injury or in the control of robot prostheses and exoskeleton devices. At the beginning of this work, we introduce a newly developed force-sensing device for measuring the COP and vGRF. Then, a model-free calibration method is proposed, leveraging Gaussian process regression (GPR) to extract COP and vGRF from raw sensor data. This approach yields remarkably low normalized root mean squared errors (NRMSEs) of 0.029 and 0.020 for COP in the mediolateral and anteroposterior directions, respectively, and 0.024 for vGRF. However, in general, learning-based calibration methods are sensitive to abnormal readings from sensing elements. To improve the robustness of the measurement, a GPR-based fault detection network is outlined for evaluating the sensing state within the fault in individual sensing elements of the force-sensing device. Moreover, a GPR-based recovery method is proposed to retrieve the sensing device's function under the fault conditions. In validation experiments, the effect of the scale factor of the threshold in the fault detection network is experimentally analyzed. The fault detection network can achieve over 90% success rate with a lower than 5 seconds delay on average in detecting the fault when the scale factor is between 1.68 and 1.90. The engagement of GPR-based recovery models under fault conditions demonstrates a substantial enhancement in COP (up to 85.0% improvement) and vGRF (up to 84.8% improvement) estimation accuracy.

关键词： Sensors Robot sensing systems Calibration Force Fault detection Vectors Training failure detection and recovery force and tactile sensing wearable device

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Learning-Based Minimally-Sensed Fault-Tolerant Adaptive Flight Control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 5198-5205页

作者： O'Connell, Michael Cho, Joshua Anderson, Matthew Chung, Soon-Jo CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

Many multirotor aircraft use redundant configurations to maintain control in the event of an actuator failure. Due to the redundancy of the system, fault isolation is inherently difficult and further compounded by complex interacting aerodynamics of the propellers, wings, and body. This letter presents a novel sparse failure identification and control correction method that does not require direct fault sensing, and instead utilizes only the vehicle's dynamic response. The method couples an l(1)-regularized representation of the failure with a deep neural network to effectively isolate faults and improve tracking control in highly dynamic environments with unmodeled aerodynamic effects and unknown actuator failures. The method also includes a control re-allocation scheme which corrects for the identified faults while maximizing control authority and maintaining nominal performance characteristics. Experimental results demonstrate the method's ability to maintain control of a multirotor aircraft by isolating motor failures and reallocating control, improving position tracking by 48 % over the baseline. This letter contributes to the development of robust fault detection and control strategies for over-actuated aircraft.

关键词： failure detection and recovery machine learning for robot control robust/adaptive control

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