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Active Multiobject Exploration and Recognition via Tactile Whiskers

IEEE TRANSACTIONS ON robotics 2022年第6期38卷 3479-3497页

作者： Xiao, Chenxi Xu, Shujia Wu, Wenzhuo Wachs, Juan Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

Robotic exploration under uncertain environments is challenging when optical information is not available. In this article, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment nonintrusively. This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery. Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.

关键词： Robot sensing systems Tactile sensors Shape Barometers Sensitivity Optical sensors Optical imaging deep learning in robotics and automation force and tactile sensing perception for grasping and manipulation reactive and sensor-based planning

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Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10750-10760页

作者： Wang, Hengli Fan, Rui Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Ophthalmol La Jolla CA 92093 USA Hong Kong Polytech Univ Dept Mech Engn Hong Kong Peoples R China

Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CNNs) have been proposed for pixelwise drivable area and road anomaly detection. In addition, some benchmark datasets, such as KITTI and Cityscapes, have been widely used. However, the existing benchmarks are mostly designed for self-driving cars. There lacks a benchmark for ground mobile robots, such as robotic wheelchairs. Therefore, in this article, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating existing state-of-the-art (SOTA) single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features. Furthermore, we propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently. The experimental results show that the transformed disparity image is the most informative visual feature and the proposed DFM-RTFNet outperforms the SOTAs. In addition, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark.

关键词： Roads Visualization Mobile robots Semantics Benchmark testing Anomaly detection Feature extraction deep learning in robotics and automation dynamic fusion mobile robots semantic scene understanding

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Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using deep Reinforcement learning

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IEEE TRANSACTIONS ON robotics 2022年第1期38卷 317-328页

作者： Rudin, Nikita Kolvenbach, Hendrik Tsounis, Vassilios Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland

In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an off-the-shelf deep reinforcement learning algorithm, we train a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of 3-D (re)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-to-real transfer, we deploy trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for 2-D microgravity experiments. The experimental results demonstrate that repetitive controlled jumping and landing with natural agility is possible.

关键词： Robots Legged locomotion Task analysis Actuators Gravity Training Kinematics deep learning in robotics and automation legged robots space robotics and automation

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Flow: A Modular learning Framework for Mixed Autonomy Traffic

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IEEE TRANSACTIONS ON robotics 2022年第2期38卷 1270-1286页

作者： Wu, Cathy Kreidieh, Abdul Rahman Parvate, Kanaad Vinitsky, Eugene Bayen, Alexandre M. MIT Informat & Decis Syst Lab Cambridge MA 02139 USA MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Univ Calif Berkeley Inst Transportat Studies Berkeley CA 94720 USA

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multivehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections). Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4-7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic-surpassing all known model-based controllers to achieve near-optimal performance-and generalize to out-of-distribution traffic densities.

关键词： Mathematical model Analytical models Vehicle dynamics Robots Uncertainty System dynamics Numerical models automation technologies for smart cities deep learning in robotics and automation deep reinforcement learning intelligent transportation systems

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Robot grasping in dense clutter via view-based experience transfer

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INTERNATIONAL JOURNAL OF INTELLIGENT robotics AND APPLICATIONS 2022年第1期6卷 23-37页

作者： Wang, Jen-Wei Li, Chia-Lien Chen, Jian-Lun Lee, Jyh-Jone Natl Taiwan Univ Dept Mech Engn Taipei 106 Taiwan

To perform object grasping in dense clutter, we propose a novel algorithm for grasp detection. To obtain grasp candidates, we developed instance segmentation and view-based experience transfer as part of the algorithm. Subsequently, we established an algorithm for collision avoidance and stability analysis to determine the optimal grasp for robot grasping. The strategy for the view-based experience transfer was to first find the object view and then transfer the grasp experience onto the clutter scenario. This strategy has two advantages over existing learning-based methods for finding grasp candidates. (1) our approach can effectively exclude the influence of noise or occlusion on images and precisely detect grasps that are well aligned on each target object. (2) our approach can efficiently find out optimal grasps on each target object and has the flexibility of adjusting and redefining the grasp experience based on the type of target object. We evaluated our approach using some open-source datasets and with a real-world robot experiment, which involved a six-axis robot arm with a two-jaw parallel gripper and a Kinect V2 RGB-D camera. The experimental results show that our proposed approach can be generalized to objects with complex shape, and is able to grasp on dense clutter scenarios where different types of objects are in a bin. To demonstrate our grasping pipeline, a video is provided at https://***/gQ3SO6vtTpA.

关键词： Perception for Grasping and Manipulation deep learning in robotics and automation

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deep REINFORCEMENT learning BASED PATH PLANNING FOR MOBILE ROBOTS USING TIME-SENSITIVE REWARD 19

DEEP REINFORCEMENT LEARNING BASED PATH PLANNING FOR MOBILE R...

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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Zhao Ruqing Lu Xin Lyu Shubin Zhang Jihuai Li Fusheng Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou 313000 Peoples R China

ISBN: (纸本)9781665493895

In the mobile robots' field, the global path planning task in known map scenarios is an urgent problem to be solved. deep Reinforcement learning (DRL), an efficient decision-making method, has been widely used to solve path-planning problems. Nonetheless, as the map size increases, the existing DRL algorithms are prone to the problem of sparse rewards. The above drawback makes the mobile robot converge slowly on the map. Even in extreme cases such as trap maps, the robot obtains the optimal convergent solution differently. For this purpose, this paper encodes the various node information in the map separately as a state description of the environment. To efficiently perform path-planning tasks for mobile robots in various complex scenarios, a time-sensitive reward function based on DRL is presented. The simulation experiments on a variety of complex environmental maps are conducted. The experimental results demonstrate the effectiveness of the proposed method. Our method ensures that the DRL algorithm is able to converges to a feasible solution quickly.

关键词： Path planning Collision avoidance deep learning in robotics and automation deep reinforcement learning (DRL) Time-sensitive reward

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Multi-Agent Pathfinding for Deadlock Avoidance on Rotational Movements 17

Multi-Agent Pathfinding for Deadlock Avoidance on Rotational...

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17th International Conference on Control, automation, robotics and Vision (ICARCV)

作者： Chan, Frodo Kin Sun Law, Yan Nei Lu, Bonny Chick, Tom Lai, Edmond Shiao Bun Ge, Ming Hong Kong Ind Artificial Intelligence & Robot Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665476874

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.

关键词： Multi-Robot Systems deep learning in robotics and automation Imitation learning Reinforcement learning MAPF-rot

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AI Co-Pilot Object Recognition for Sensory Soft Robotic Grippers

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IEEE TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2024年

作者： Zhang, Tengxin Liu, Xiaotong Song, Yang Tang, Yan Xia, Haisheng Li, Zhijun Shi, Peng Tongji Univ Shanghai Yangzhi Rehabil Hosp Translat Res Ctr Shanghai Sunshine Rehabil CtrSch Mech Engn Shanghai 201804 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230026 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Tongji Univ Sch Mech Engn Shanghai 200092 Peoples R China Univ Adelaide Sch Elect & Mech Engn Adelaide SA 5005 Australia Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary

Developing non-visual sensing and intelligent recognition technologies is crucial for enhancing the manipulation performance of robots in dim or obstructed environments. Although precise object recognition has been extensively researched for rigid manipulators, the adoption of these techniques in soft robotic systems has been limited by the high modulus or low sensitivity of existing sensors (gauge factor/Young's modulus < 10 kPa(-1) ). Meanwhile, these systems necessitate advanced artificial intelligence(AI) algorithms to effectively process multiple sensing data. In this study, we utilize newly developed soft sensors and AI algorithm to establish a biomimetic perceptual soft gripper system capable of sensing and generating category object information during the grasping task. A strain/pressure bimodal sensor, mimicking the exceptional softness (Young's modulus < 10 kPa) and high sensitivity (gauge factor > 2000) of human skin, has been developed and seamlessly integrated into a three-finger soft robotic gripper. A Swin Transformer network was developed to learn rules from bimodal data acquired from sensors and generate category information of the grasping objects. The perceptual gripper system exhibited superior recognition accuracy compared to previously reported systems, achieving an impressive 94.9% accuracy in categorizing 18 objects of varying shapes and sizes. We believe this advancement unlocks soft machines' potential for automated applications. Note to Practitioners-Non-visual sensing and recognition techniques for soft robotic grippers are valuable for operation in dimly lit or obstructed environments. Traditional rigid sensor have limitations when applied to soft robotic systems due to their high modulus. This research developed novel ultra-soft and high sensitive strain/pressure bimodal sensors integrated into a three-fingered soft robotic gripper. Meanwhile, an AI algorithm applicable to the soft gripper system was developed to process the sensing dat

关键词： Sensors Grippers Robot sensing systems Object recognition Soft robotics Grasping Artificial intelligence Strain Skin Sensitivity Soft sensors and actuators perceptual soft gripper object recognition deep learning in robotics and automation

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LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation

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IEEE robotics AND automation LETTERS 2021年第2期6卷 2799-2806页

作者： Chen, Jingdao Kira, Zsolt Cho, Yong K. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

3D point cloud segmentation is an important function that helps robots understand the layout of their surrounding environment and perform tasks such as grasping objects, avoiding obstacles, and finding landmarks. Current segmentation methods are mostly class-specific, many of which are tuned to work with specific object categories and may not be generalizable to different types of scenes. This research proposes a learnable region growing method for class-agnostic point cloud segmentation, specifically for the task of instance label prediction. The proposed method is able to segment any class of objects using a single deep neural network without any assumptions about their shapes and sizes. The deep neural network is trained to predict how to add or remove points from a point cloud region to morph it into incrementally more complete regions of an object instance. Segmentation results on the S3DIS and ScanNet datasets show that the proposed method outperforms competing methods by 1%-9% on 6 different evaluation metrics.

关键词： deep learning in robotics and automation object detection segmentation and categorization semantic scene understanding

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A Single-Stream Segmentation and Depth Prediction CNN for Autonomous Driving

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IEEE INTELLIGENT SYSTEMS 2021年第4期36卷 79-85页

作者： Aladem, Mohamed Rawashdeh, Samir A. Univ Michigan Dearborn Dearborn MI 48128 USA

Convolutional neural networks (CNN) have been used successfully in solving many challenging visual perception tasks facing mobile robots and self-driving cars. To facilitate deploying such models on embedded hardware onboard mobile robots that have limited resources, multitask learning approaches have become common. In typically used multitask learning, a shared encoder network extracts features from inputs whereas multiple task-specific decoders transform these features into their target output. However, properly combining different tasks' losses into the final network loss such that each task is making progress learning is a major challenge in these approaches. In this article, we present an innovative approach to extend a typical single-task network with the capability of performing two tasks without multiple decoders, i.e., a single-stream two-task network. The two output tasks are semantic segmentation and monocular depth prediction which are essential tasks in visual perception for autonomous driving. The method is centered on solving semantic segmentation with a regression loss function rather than a classification one. With our approach, we seize multitask learning benefits of reduced overhead and enhanced generalization while alleviating the need to balance different loss functions. Experimental evaluations with baseline single tasks and a multitask network are presented.

关键词： Autonomous vehicles Computer Vision for Transportation deep learning in robotics and automation Semantic Scene Understanding

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