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An Improved UAV Path Optimization Algorithm for Target Accurately and Quickly Localization

An Improved UAV Path Optimization Algorithm for Target Accur...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Rui Liang Kai Wu Sheng Xu Tiantian Xu Xiaoyi Ma Lianyu Fu City West Power Supply Branch State Grid Tianjin Electric Power Company Tianjin China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China State Grid Tianjin Information & Telecommunication Company Tianjin China

ISBN: (纸本)9781665402460

Angle-of-arrival (AOA) target localization using the unmanned aerial vehicle (UAV) has been widely applied in many practical applications. To localize an invasive target quickly and accurately, both the estimation and UAV path optimization algorithms are required. This paper focuses on developing a path optimization method to improve the target estimation performance. Firstly, the problem formulation of AOA target localization is introduced. Secondly, the classical pseudolinear Kalman filter (PLKF) and the gradient-based path optimization are presented. Thirdly, we analyze the problems that existed in the previous methods and propose an improved gradient-descent path optimization algorithm combined with a simple grid search method. Finally, the simulation examples verify the effectiveness of the proposed methods.

关键词： Location awareness Adaptation models Three-dimensional displays Target tracking Search methods Estimation Optimization methods

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Learning-based adaptive estimation for AOA target tracking with non-gaussian white noise

Learning-based adaptive estimation for AOA target tracking w...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Xu, Sheng Ou, Yongsheng Wu, Xinyu Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781728163215

In angle-of-arrival (AOA) target tracking, the target states are estimated using the noisy angular measurements. The non-Gaussian measurement noise is common in practical applications and will decrease the estimation accuracy significantly. This paper is to reduce the negative impacts of the non-Gaussian white noise (NGWN), which is modeled by multiple mixture Gaussian white noises, and guarantee the estimation accuracy. A learning-based adaptive extended Kalman filter (EKF) for AOA target tracking is developed in the NGWN environment. In the proposed method, the extreme learning machine (ELM) using a three-layer neural network is applied to identify the characters of measurement noise at each sampling time. Consequence, the EKF will consider the noise characters by the trained ELM, and make the corresponding real-time adjustments to improve the tracking performance. The effectiveness of the proposed method is verified by simulation examples. © 2019 IEEE.

关键词： Target tracking

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Obstacle avoidance with reinforcement learning and adaptive resonance theory

Obstacle avoidance with reinforcement learning and adaptive ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ye, Lingjian Zhou, Yimin Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (纸本)9781728163215

The reinforcement learning (RL) of the autonomous mobile agent is one of the actual research topics. It permits mobile agents to interact constantly with their environment and to avoid obstacles. First, this paper presents an algorithm which integrates Deep Deterministic Policy Gradient (DDPG) algorithm and Fuzzy Adaptive Resonance Theory (ART) in order to improve generalization performance of RL. Then the curiosity is introduced to integrate with the first proposed algorithm to solve the problem of slow convergence caused by Fuzzy ART. Results of the simulation experiments demonstrate the effectiveness of the proposed algorithm. It shows that the algorithms perform well in both low-dimension and high-dimension state space. © 2019 IEEE.

关键词： Reinforcement learning

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A novel multi-scale fourier descriptor based on plane orthogonal vectors for fast shape retrieval

A novel multi-scale fourier descriptor based on plane orthog...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ma, Jiajun Zhu, Jiafeng Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong Province China

ISBN: (纸本)9781728163215

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors;Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor. © 2019 IEEE.

关键词： Topology

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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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Low Light Enhancement by Unsupervised Network*

Low Light Enhancement by Unsupervised Network*

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yangyang Qu Yongsheng Ou Rong Xiong Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper focuses on unsupervised low-light enhancement methods for practical applications such as automated driving, surveillance cameras, and so on. This paper proposes an unsupervised method that is independent of paired samples and works well to solve the image darkness problem. Furthermore, to increase the brightness of the image and restore the information hidden in the dark, an auxiliary attention module is incorporated into our model. This proposed module can help generate images with good edges and make the image colors and details richer. The experimental results show that the proposed method works well for both image enhancement and noise control.

关键词： Image edge detection Image enhancement Lighting Generators Visual effects Histograms Brightness

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Low illumination enhancement for object detection in self-driving

Low illumination enhancement for object detection in self-dr...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Qu, Yangyang Ou, Yongsheng Xiong, Rong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-MachineIntelligence-Synergy Systems ShenzhenInstitutes of Advanced Technology China

ISBN: (纸本)9781728163215

Object detection plays an important role in the field of self-driving. Illumination has a great impact on object detection, but most of the current methods do not solve the problem of object detection in poor light environment well. We propose a network that can optimize the image conversion which is based on the Cycle Generative Adversarial Networks (CycleGAN). We redesign the discriminator network of Cycle-GAN, add additional discriminators, optimize multiple parts of the network such as loss functions, and add object detection networks after converting the network. The Robot Car dataset of Oxford University is utilized to verify the effectiveness of the proposed method, and the results proved that our method can significantly improve the detection accuracy and increase the detected object number in low illumination environment. © 2019 IEEE.

关键词： Image enhancement

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A method of cliff detection in robot navigation based on multi-sensor

A method of cliff detection in robot navigation based on mul...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Zhilong Su Can Wang Yuxiao Li Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technolog Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposed a method to detect cliff edge by using an RGB-D camera, and then to fuse the cliff information with LiDAR, finally to provide navigation information for robot autonomous exploration and autonomous movement. Compared with the prior methods which set sonar or infra-red sensor in the bottom of the robot, our method could detect cliff earlier by set the RGB-D camera forward. In cliff detection, we use region growing algorithm and the normal vector of plane to segment out ground plane in point cloud, then extract the boundary points of ground, and we proposed an Edge Point Filter (EPF) to get the edge points of cliff, then use the edge points to simulate LiDAR data and combine with real LiDAR data to mark the untraversable area. We verified the practicability of this method in simulation, and verified the accuracy with real sensors.

关键词： Laser radar Robots Three-dimensional displays Robot sensing systems Indexes Cameras Image edge detection

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Development of an assist upper limb exoskeleton for manual handling task

Development of an assist upper limb exoskeleton for manual h...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Yan, Zefeng Yi, Haoyuan Du, Zihao Huang, Tiantian Han, Bin Zhang, Lufeng Peng, Ansi Wu, Xinyu State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology China CAS Key Laboratory of Human-Manchine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Science Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

In this paper we describe the construction of a wearable upper arm exoskeleton to provide support for industrial workers who engaged in repetitive handling tasks and patients who full or partial loss of function in their upper limb due to accident injuries, spinal cord and strokes. We use flexible rope driven by a motor to realize the shoulder and elbow joints linkage, use ball screw to realize the self-locking in any position to providing assist for users. This novel design not only saves energy but also make the whole system portable. The total weight of the prototype is only 2 kg. The exoskeleton is connected upper limb with 3 bandages, and users take about 30 seconds to put it on and 20 seconds to put the exoskeleton off without any help. The PD adaptive control method is applied to the proposed exoskeleton to track expected trajectories. Five healthy people participated in data acquisition. Upper limb muscle integrated electromyography (IEMG) of the deltoid muscle(DM), the biceps femoris muscle (BFW), the femoral muscle (FM), the femoral wrist flexor (FWF) were compared in continuous handling for four different loads (0, 1.5, 3, 4.5kg). The average assistance efficiency were 39.1%, 41.5%, 36.5% and 40.6% for the DM, BFM, FM and FWF respectively. The experimental data show that the upper limb exoskeleton can provide assist for workers and protect their upper limbs injuries in long-term handling task. © 2019 IEEE.

关键词： Muscle

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Intelligent Visual Servoing Using Learning-Based Grasping Configurations and Adaptive Controller Gain

Intelligent Visual Servoing Using Learning-Based Grasping Co...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jiqing Li Jianghua Duan Yongsheng Ou Sheng Xu Zhiyang Wang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

For the traditional image-based visual servoing (IBVS) system, the desired configurations of grasping are predefined for the robot and the gain λ in the control law is fixed. While training the robots with new skills, the predefined grasping configurations may increase the workload; meanwhile, the fixed controller gain λ may slow down the convergence of the system. To this end, in this paper, an intelligent IBVS system using learning-based grasping configurations and adaptive controller gain is presented. With the Gaussian Mixed Model (GMM), the robot is capable of learning the desired grasping configurations through a certain number of demonstrations. To accelerate the convergence of the IBVS system, a fuzzy logic (FL) unit is applied to adjust the gain λ. The simulation results demonstrate that the proposed approach enables the IBVS system gain the learning ability on grasping configurations, besides accelerating the convergence.

关键词： Grasping Robots Fuzzy logic Robot kinematics Convergence Cameras Adaptation models

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