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Multi-agent Gaussian Process Motion Planning via Probabilistic Inference

IFAC-PapersOnLine 2018年第22期51卷 160-165页

作者： Petrovi, Luka Markovi, Ivan Seder, Marija University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

This paper deals with motion planning for multiple agents by representing the problem as a simultaneous optimization of every agent's trajectory. Each trajectory is considered as a sample from a one-dimensional continuous-time Gaussian process (GP) generated by a linear time-varying stochastic differential equation driven by white noise. By formulating the planning problem as probabilistic inference on a factor graph, the structure of the pertaining GP can be exploited to find the solution efficiently using numerical optimization. In contrast to planning each agent's trajectory individually, where only the current poses of other agents are taken into account, we propose simultaneous planning of multiple trajectories that works in a predictive manner. It takes into account the information about each agent's whereabouts at every future time instant, since full trajectories of each agent are found jointly during a single optimization procedure. We compare the proposed method to an individual trajectory planning approach, demonstrating significant improvement in both success rate and computational efficiency. © 2018

关键词： Motion planning Computational efficiency Continuous time systems Differential equations Gaussian distribution Gaussian noise (electronic) Multi agent systems One dimensional Optimization Stochastic systems Trajectories White noise Factor graphs Numerical optimizations Optimization procedures Probabilistic inference Simultaneous optimization Stochastic differential equations Trajectory optimization Trajectory Planning

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Contact-Aware Controller Design for Complementarity systems

Contact-Aware Controller Design for Complementarity Systems

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IEEE International Conference on robotics and Automation (ICRA)

作者： Alp Aydinoglu Victor M. Preciado Michael Posa Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on three different underactuated, multi-contact robotics problems.

关键词： Lyapunov methods Control systems Force Dynamics Task analysis Tactile sensors

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Learning stability certificates from data

arXiv

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arXiv 2020年

作者： Boffi, Nicholas M. Tu, Stephen Matni, Nikolai Slotine, Jean-Jacques E. Sindhwani, Vikas Google Brain Robotics John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Electrical and Systems Engineering University of Pennsylvania United States Nonlinear Systems Laboratory Massachusetts Institute of Technology United States

Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property. However, algorithms for synthesizing certificate functions typically require a closed-form analytical expression of the underlying dynamics, which rules out their use on many modern robotic platforms. To circumvent this issue, we develop algorithms for learning certificate functions only from trajectory data. We establish bounds on the generalization error – the probability that a certificate will not certify a new, unseen trajectory – when learning from trajectories, and we convert such generalization error bounds into global stability guarantees. We demonstrate empirically that certificates for complex dynamics can be efficiently learned, and that the learned certificates can be used for downstream tasks such as adaptive control. Copyright © 2020, The Authors. All rights reserved.

关键词： Trajectories

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WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle Avoidance in Dynamic Unmapped Environments Using a Laser Scanning System (LiDAR)

WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle...

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IEEE Latin American robotics Symposium, LARS

作者： Jonathan Cerbaro Dieisson Martinelli André Schneider de Oliveira João Alberto Fabro ederal University of Technology - Paraná (UTFPR) Graduate Program on Electrical Engineering and Applied Informatics (CPGEI) Advanced Laboratory of Embedded Systems and Robotics (LASER) Curitiba Paraná Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Fuzzy logic is a control technique commonly applied in mobile robotics. This paper contains the analysis of three implementations of control systems for obstacle avoidance of a mobile robot. The simulation environment simulate situations with both fixed and dynamic obstacles - like people walking - aiming to compare the fuzzy control approach most suitable to avoid different kinds of obstacles. The analyzed approaches differ by segmenting the sensor data (provided by a LiDAR sensor) into three regions, five regions or by considering only the position of the nearest obstacle. The three approaches were tested in scenarios containing different obstacles and in a simulation where the robot delivers to tables in a dynamic environment, simulating a social event as an application example of a waiter robot.

关键词： Robot sensing systems Robots Collision avoidance Robot kinematics Laser radar Fuzzy logic Input variables

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fNIRS Feature Extraction and Classification in Grip-Force Tasks

fNIRS Feature Extraction and Classification in Grip-Force Ta...

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IEEE International Conference on robotics and Biomimetics

作者： Jinrui Liu Ting Song Zhilin Shu Jianda Han Ningbo Yu College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (纸本)9781665405362

It has been of increasing interest to decode the movements based on the changes in hemoglobin concentration in the cerebral cortex that can be measured by functional near-infrared spectroscopy (fNIRS). Existing work were mainly focused on motor imagery tasks for the advantages of fNIRS on cognitive activation detection, and motor tasks were not well studied. This paper investigated fNIRS feature extraction and classification methods for grip-force tasks. Two methods were implemented and compared. The first method took the classic machine learning approach. Features were extracted with respect to the physiological characteristics of fNIRS signals, including the mean, area and slope features. Then, support vector machine was used for classification of grip-force tasks with the extracted features. The second method took the deep learning approach without extracting features. An LSTM-based network was constructed to take in the fNIRS data and output the classified task type. The two methods were tested and compared with an grip-force task fNIRS dataset. The classic machine learning method with extracted features achieved the accuracy of 71.70%, and the LSTM-based deep learning method achieved 82.86%. The results demonstrate the feasibility and potential of fNIRS-based decoding of motor tasks.

关键词： Deep learning Support vector machines Cerebral cortex Stroke (medical condition) Feature extraction Decoding Task analysis

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Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”

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IEEE robotics and Automation Letters 2024年第10期9卷 8426-8426页

作者： Lorenzo Lamberti Elia Cereda Gabriele Abbate Lorenzo Bellone Victor Javier Kartsch Morinigo Michał Barciś Agata Barciś Alessandro Giusti Francesco Conti Daniele Palossi Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Dalle Molle Institute for Artificial Intelligence USI-SUPSI Lugano Switzerland Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE Integrated Systems Laboratory ETH Zürich Zürich Switzerland

Presents corrections to the article “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”.

关键词： Robots Technological innovation IEEE Associate Members

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Human intention recognition for human aware planning in integrated warehouse systems

arXiv

引用

arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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Research on Elderly Fall Prediction for Walking Posture of Elderly-Assistant Robot 2019

Research on Elderly Fall Prediction for Walking Posture of E...

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Proceedings of the 2019 2nd International Conference on Control and Robot Technology

作者： Liangliang Li Xiaodong Zhang Xiaoqi Mu Haipeng Xu Institute of Robotics & Intelligent Systems School of Mechanical Engineering Xi'an Jiaotong University Xi'an China Shaanxi Key Laboratory of Intelligent Robot Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781450372527

A prediction method of elderly fall based on RBF neural network and multi-sensor information fusion was proposed in this paper, which could help the user of elderly-assistant robot walk outside safe and reduce the damage caused by falls. Firstly, overall scheme of the elderly fall prediction system was designed. Tactile sensors, tri-axial acceleration sensor and gyroscope were used to collect the touch information of user's hands, the acceleration information and angle of inclination information of human trunk, respectively. Then, three kinds of feature information were extracted separately and fused up by RBF neural network to get probability of elderly fall. If the probability exceeded the threshold, it was judged that the human body had a tendency to fall. Finally, experimental system construction and verification were carried out and the results showed that the prediction method of the user fall was reliable, and the overall prediction accuracy rate was 98%. Among them, the prediction accuracy of normal samples and user fall samples were 100% and 96%), respectively. This method can predict the user fall more accurately, and it provides some guarantees for preventing elderly fall.

关键词： RBF neural network multi-sensor information fusion threshold fall prediction

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Research on Elderly Fall Prediction for Walking Posture of Elderly-Assistant Robot 2

Research on Elderly Fall Prediction for Walking Posture of E...

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2nd International Conference on Control and Robot Technology, ICCRT 2019

作者： Li, Liangliang Zhang, Xiaodong Mu, Xiaoqi Xu, Haipeng Institute of Robotics and Intelligent Systems School of Mechanical Engineering Xi'an Jiaotong University Xi'an China Shaanxi Key Laboratory of Intelligent Robot Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781450372527

关键词： Forecasting

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Computationally efficient dense moving object detection based on reduced space disparity estimation

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IFAC-PapersOnLine 2018年第22期51卷 360-365页

作者： Popovi, Goran Hadviger, Antea Markovi, Ivan Petrovi, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Computationally efficient moving object detection and depth estimation from a stereo camera is an extremely useful tool for many computer vision applications, including robotics and autonomous driving. In this paper we show how moving objects can be densely detected by estimating disparity using an algorithm that improves complexity and accuracy of stereo matching by relying on information from previous frames. The main idea behind this approach is that by using the ego-motion estimation and the disparity map of the previous frame, we can set a prior base that enables us to reduce the complexity of the current frame disparity estimation, subsequently also detecting moving objects in the scene. For each pixel we run a Kalman filter that recursively fuses the disparity prediction and reduced space semi-global matching (SGM) measurements. The proposed algorithm has been implemented and optimized using streaming single instruction multiple data instruction set and multi-threading. Furthermore, in order to estimate the process and measurement noise as reliably as possible, we conduct extensive experiments on the KITTI suite using the ground truth obtained by the 3D laser range sensor. Concerning disparity estimation, compared to the OpenCV SGM implementation, the proposed method yields improvement on the KITTI dataset sequences in terms of both speed and accuracy. © 2018

关键词： Computer vision Computational complexity Computational efficiency Motion estimation Object detection Object recognition Stereo image processing Stereo vision 3D laser range sensors Computationally efficient Computer vision applications Dense disparity estimation Detecting moving objects Moving object detection Semi global matching Single instruction multiple data instructions

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