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A real-time cooperative sweeping strategy for multiple cleaning robots

A real-time cooperative sweeping strategy for multiple clean...

引用

ieee international symposium on Intelligent Control (ISIC)

作者： Chaomin Luo S.X. Yang Advanced Robot ics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph ONT Canada

In this paper, a cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying and unstructured environment is proposed using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. Multiple cleaning robots can cooperate to achieve a common sweeping goal effectively. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots.

关键词： Cleaning Orbital robotics Path planning Robot sensing systems Intelligent robots Neural networks robotics and automation computational modeling Landmine detection System recovery

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A framework for learning biped locomotion with dynamical movement primitives

A framework for learning biped locomotion with dynamical mov...

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ieee-RAS international Conference on Humanoid Robots

作者： Jun Nakanishi Jun Morimoto G. Endo G. Cheng S. Schaal M. Kawato ATR Computational Neuroscience Laboratories Sarku Kyoto Japan Sony Intelligence Dynamics Laboratories Inc. Shinagawa Tokyo Japan Department of Computer Science and Neuroscience University of Southern California Los Angeles CA USA

This article summarizes our framework for learning biped locomotion using dynamical movement primitives based on nonlinear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural humanlike locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller. Furthermore, we demonstrate that phase resetting contributes to robustness against external perturbations and environmental changes by numerical simulations and experiments.

关键词： Legged locomotion Oscillators Frequency Robot kinematics Laboratories robotics and automation Numerical simulation Mechanical systems computational intelligence Computer science

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Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules

Skills Composition Framework for Reconfigurable Cyber-Physic...

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international Conference on Emerging Technologies and Factory automation (ETFA)

作者： Aleksandr Sidorenko Achim Wagner Martin Ruskowski Innovative Factory System German Research Center for Artificial Intelligence Kaiserslautern Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The behavior trees (BTs) framework is a novel approach that enables the intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers. This shows the instantiation of the proposed framework with specific industrial technology to enable its evaluation by the automation community.

关键词： Adaptation models Protocols Software architecture computational modeling Production Computer architecture Software Artificial intelligence Robots Manufacturing systems

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Agreement assessment of biochemical pathway models by structural analysis of their intersection

Agreement assessment of biochemical pathway models by struct...

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international symposium on computational intelligence and Informatics

作者： Tatjana Rubina Martins Mednis Egils Stalidzans Biosystems Group Latvia University of Agriculture Jelgava Latvia

In case of model development, it would be an advantage to assess the quality of available models looking for the best one or to find suitable parts of a published model to build a new one. The differences or contradictions in reconstructions can indicate the level of agreement between different authors about the topic of interest. The intersecting part of models can reveal also the differences in the scope of the models. Two pairs of models from BioCyc database were analyzed: 1) the Escherichia coli models ecol199310cyc and ecol316407cyc and 2) the Saccharomyces cerevisiae models iND750 and iLL672. The ModeRator software tool is used to compare models and generate their intersection model. The structural parameters of models are analyzed by the software BINESA. The study reveals very different parameters of the intersections of the pairs of the E. coli and the S. cerevisiae models. The models built by the same group of authors like in the case of E. coli is selected as an example of a high agreement between models and can be interpreted as a consensus part of two initial models. The intersection of the S. cerevisiae models demonstrates very different structural properties and the intersection model would not be able to function even after significant improvement. The structural analysis of the pairs of original models and their intersections is performed to determine which structural parameters can be used to determine a poor agreement between the pairs of models. It is concluded that an application of the automated comparison and intersection generation of two models can give a fast insight in the similarity of the models to find out the consensus level in modelling of metabolism of a particular organism. This approach can be used also to find similarities between the models of different organisms. automation of intersection creation and structural analysis are enabling technologies of this approach.

关键词： Biological system modeling Analytical models Structural engineering computational modeling Biochemistry Substrates Software

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Geospatial intelligence for Individual Crop Detection and Anomaly Monitoring

Geospatial Intelligence for Individual Crop Detection and An...

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ieee international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Freda Elikem Dorbu Leila Hashemi-Beni Department of Computational Data Science and Engineering North Carolina A&T State University Department of Built Environment Geomatics Program North Carolina A&T State University

Acquisition of geospatial data by UAV has been acknowledged as an effective method of attaining reliable and quick high-resolution remote sensing data for analysis and decision-making in different applications such as agriculture as it produces timely results, and it is affordable. UAVs coupled with other technological applications such as robotics, computing and deep learning facilitate the execution of precision agriculture. The execution of individual tree identification enables vivid description of the crop peak height and canopy for accurate estimation of issues related to crop growth such as biomass reduction, lodging, and stunted growth. A geospatial intelligence algorithm was developed to determine the lodging state of the crops. First, the optical imagery (RGB) was automatically annotated using maximum likelihood classification algorithm for faster acquisition of ground truth and to verify with the manually annotated image data for deep learning training. The acquired images were split for training of fine-tuned three deep learning methods including U-net, U-net++ and attention-residual Unet. The results show that each model has the tendency to learn patterns in data and predict lodged crops. Results from the analysis were validated by visual assessment of the time-series aerial images and validation data acquired from the ground truth data using k-fold cross validation approach.

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Manipulation and dynamic mechanical testing of microscopic objects using a tele-micro-robot system

Manipulation and dynamic mechanical testing of microscopic o...

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ieee international Conference on robotics and automation (ICRA)

作者： I.W. Hunter S. Lafontaine P.M.F. Nielsen P.J. Hunter J.M. Hollerbach Biomed. Eng. McGill Univ. Montreal Que. Canada Biomedical Engineering McGill University Montreal Canada Artificial Intelligence Laboratory MIT Cambridge MA USA

A microrobot having two high-performance parallel drive limbs has been developed for manipulation, surgery, and dynamic mechanical testing of very small objects such as single living cells. The end-points of each limb move in overlapping spherical workspaces of 1 mm diameter with minimum open- and closed-loop movements of 1 nm and 10 nm, respectively. With optimal nonlinear model-based controllers the limbs can move at up to 2 m/s relative to each other. A variety of end effectors, including ferroelectric polymer microgrippers, may be attached to the limbs to permit cell manipulation. A 3D laser vision system with resolution of 50 to 100 nm has been developed to provide the microrobot with volume images containing magnitude, phase, polarization, and special information. A macro version of the microrobot has been built to enable force-reflecting teleoperation of the microrobot. The telemicrorobot system permits both microscopic objects and continuum models to be felt. A high-performance parallel computer has been designed to meet the substantial computational and control requirements of the system.< >

关键词： Manipulator dynamics Testing Microscopy Concurrent computing Surgery Optimal control End effectors Ferroelectric materials Polymers Grippers

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Motion Planning of Tree Fruit Robot Picker in Presence of Obstacles Using Heuristic Approach

Motion Planning of Tree Fruit Robot Picker in Presence of Ob...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Aleksandar Rodić Jelena Kljajić Robotics department Mihajlo Pupin Institute Serbia Serbia

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

The paper introduces a heuristic approach for planning the motion of fruit-picking robots operating among various, randomly placed obstacles within the task's operational space. A linear robot, equipped by three degrees of Cartesian-type freedom, has been designed for this task, featuring an additional telescopic arm and a specialized gripper. This gripper is uniquely fitted with two rotational joints at the wrist, enabling precise orientation adjustment of the tool in relation to the fruit on the branches. The planning of the robot's movement leverages machine vision and artificial intelligence algorithms, including artificial neural networks and fuzzy logic for decision making. This heuristic approach to the robot picker's movement planning draws inspiration from human-inspired techniques used in manual fruit picking on fruit plantations. The methodology employed utilizes environmental data to determine the shortest paths for the robot's gripper to reach specific targets, and to assess the relative position (orientation) of the actuator in relation to the target or obstacles encountered along the way (such as branches, clusters of leaves, trees, etc.). The effectiveness of this movement planning methodology has been confirmed through simulation models of the robot and its environment.

关键词： Wrist Machine vision Plantations Manuals Manipulators Planning Grippers

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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ieee international Conference on robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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Two-Stream Sparse Feature Non-local Spatiotemporal Residual Convolutional Neural Network for Human Action Recognition

Two-Stream Sparse Feature Non-local Spatiotemporal Residual ...

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robotics & Intelligent Manufacturing Technology (ISRIMT), international symposium on

作者： Huimin Qian Junwei Huang College of Artificial Intelligence and Automation Hohai University Changzhou China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

Three-dimensional convolutional neural networks and two-stream convolutional neural networks have distinct advantages for recognizing human actions in videos, while both are lack of modeling capability of long-range dependencies in video frames. In view of this, a Two-Stream Pruned Sparse Feature Non-local Spatiotemporal Residual Convolutional Neural Network (TPSFNLST-ResCNN) is proposed in this paper. In which, ST-ResCNN with the presented sparse feature non-local module (SFNL) is applied both in spatial stream and temporal stream. And SFNLST-ResCNN is then compressed based on a channel pruning scheme to reduce the parameters. Among which, the SFNL module can improve the capability of extracting the long-range dependencies information of human actions under less computing load. Experimental results show that the proposed human action recognition model achieves recognition accuracies of 98.36% and 74.70% on the UCF101 and HMDB51 public datasets, respectively. Compared with existing methods, the proposed model offers the advantages of less parameters and higher recognition accuracy.

关键词： Solid modeling Accuracy computational modeling Spatiotemporal phenomena Manufacturing Convolutional neural networks Human activity recognition Videos Load modeling Plugs

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Predicting trust in human control of swarms via inverse reinforcement learning

Predicting trust in human control of swarms via inverse rein...

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ieee international Workshop on Robot and Human Communication (ROMAN)

作者： Changjoo Nam Phillip Walker Michael Lewis Katia Sycara Robotics Institute Carnegie Mellon University Pittsburgh PA USA School of Information Science University of Pittsburgh PA USA

In this paper, we study the model of human trust where an operator controls a robotic swarm remotely for a search mission. Existing trust models in human-in-the-loop systems are based on task performance of robots. However, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since task performance of swarms is not clearly perceivable by humans. We formulate trust as a Markov decision process whose state space includes physical parameters of the swarm. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from a single demonstration. The learned behaviors are used to predict the trust level of the operator based on the features of the swarm.

关键词： Robot kinematics Robot sensing systems Learning (artificial intelligence) Training computational modeling

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