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Autonomous eVTOL:A summary of researches and challenges

Green Energy and intelligent Transportation 2024年第1期3卷 55-69页

作者： Senwei Xiang Anhuan Xie Minxiang Ye Xufei Yan Xiaojia Han Hongjiao Niu Qiang Li Haishan Huang Research Center for Intelligent Robotics Zhejiang LabHangzhou 311121China TianMuShan Laboratory Hangzhou 310023China

Due to the rising concept of advanced air mobility(AAM),electric vertical take-off and landing(eVTOL)aircraft has become the hotspot for academic research and commercial *** paper provides a comprehensive review of latest researches related to autonomous *** examines key technologies involved in autonomous eVTOL,including automated flight control,sensing&perception,safety&reliability,and decision *** also addresses the technical,regulatory,and societal challenges associated with the wholesale adoption of autonomous eVTOL into *** paper concludes with a discussion of future trends and recommendations,including the importance of integration with air traffic management,urban infrastructure and human–machine *** aims to be a useful resource for those involved in the research,policy,and industry of autonomous eVTOL technology.

关键词： eVTOL AAM Autonomy Key technology Challenge

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Wang, Wen Lin, Zheyuan Ji, Shanshan Li, Te Gu, Jason Wan, Minhong Zhang, Chunlong Research Center for Intelligent Robotics Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350325706

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset. © 2023 IEEE.

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Accelerated Unfolding Network for Medical Image Reconstruction with Efficient Information Flow 16th

Accelerated Unfolding Network for Medical Image Reconstruct...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Qin, Mengjie Yang, Rong Wan, Minhong Zhang, Chunlong Li, Te Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9789819964888

Deep unfolding networks (DUNs) have become mainstream for many medical image reconstruction tasks due to their exceptional interpretability and high performance. Unlike black-box deep neural networks, DUNs can provide insight into the intermediary steps of the reconstruction process. Extensive work has been done to study classical optimization algorithms. However, there are several issues that still require further exploration, including the unfolding implications of accelerated optimization algorithms and the performance bottlenecks in unfolding algorithms. To tackle these two concerns, this paper initially validates the extent of performance enhancement achieved by accelerated variation of ADMM, as compared to the original method. As for the second issue, we point out that the coarse information fusion operations utilized in existing unfolding networks primarily impede their performance (e.g., simple addition and subtraction). Based on this, we design a simple, reasonable yet effective accelerated ADMM-based unfolding framework, which integrate multi-channel information into existing DUNs. Additionally, the developed efficient feature aggregation strategy can further enhance the performance of DUNs. We demonstrate, through MRI accelerated reconstruction experiments, that the proposed framework outperforms state-of-the-art DUNs while utilizing fewer parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance imaging

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Free Performance Boost for Voting-based 3D Object Detection in Point Clouds

Free Performance Boost for Voting-based 3D Object Detection ...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Yang, Rong Qin, Mengjie Lin, Zheyuan Zhang, Chunlong Li, Te Wan, Minhong Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9798350325706

Recently, detecting objects from 3D point clouds based on the voting mechanism has received increasing attention. Various improvements such as RGB feature embedding, context information fusion and bounding box refinement have been developed to further boost the performance of voting-based models since VoteNet was proposed. In this work, we revisit the VoteNet model and point out the significant negative impact brought by votes from background, which has never been discussed properly in previous research as far as we know. In order to suppress votes from the background to obtain purer vote clusters, we redesign the loss function for the training of voting module of voting-based 3D detector. Experiments on the challenging SUN RGB-D dataset show that the new loss function can significantly boost the detection performance of existing state-of-the-art voting-based 3D detectors with no additional computational overhead. © 2023 IEEE.

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KGGPT: Empowering Robots with OpenAI’s ChatGPT and Knowledge Graph 16th

KGGPT: Empowering Robots with OpenAI’s ChatGPT and Knowle...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Mu, Zonghao Zhao, Wenyu Yin, Yue Xi, Xiangming Song, Wei Gu, Jianjun Zhu, Shiqiang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9789819964949

This paper presents a study on using knowledge graph with ChatGP for robotics applications, called KGGPT. Traditional planning methods for robot tasks based on structured data and sequential actions, such as rosplan, have limitations such as limited data range and lack of flexibility to modify behaviors based on user feedback. Recent research has focused on combining AI planning with large language models (LLMs) to overcome these limitations, but generated text may not always be consistent with real-world physics and the robot skills to perform physical actions. To address these challenges, we propose KGGPT, a system that incorporates prior knowledge to enable ChatGPT for a variety of robotic tasks. KGGPT extracts relevant knowledge from the knowledge graph, generates a semantic description of the knowledge, and connects it to ChatGPT. The gap between the knowledge of ChatGPT and actual service environments is addressed by using the knowledge graph to model robot skills, task rules, and environmental constraints. The output is a behavior tree based on robot skills. We evaluate our method in an office setting and show that it outperforms traditional PDDL planning and a separate ChatGPT planning scheme. Additionally, our system reduces programming effort for applications when new task requirements arise. This research has the potential to significantly advance the field of robotics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Knowledge graph

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DIF-LIM: A Dual Information Flow Network for Low-Light Image Enhancement

DIF-LIM: A Dual Information Flow Network for Low-Light Image...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Qin, Mengjie Yang, Rong Lin, Zheyuan Xu, Nuo Li, Te Wan, Minhong Zhang, Chunlong Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9798350325706

Low-light image enhancement (LIE) aims to enhance image contrast and recover fine details for images captured in low-light conditions. However, the limitations of using a single image and manually defined priors lead to inadequate information and limited adaptability, resulting in the failure to reveal image details effectively. To tackle this problem, we propose an unsupervised dual information flow LIE model that learns adaptive priors from low-light image pairs. The model is based on the assumption that priors can be learned from pairs of images. Moreover, a simple self+supervised model is designed to perform feature processing on the original input and further processing on the image to avoid the suboptimal limitations of the model. As a result, our model exhibits superior performance on the LIE task compared to other algorithms. As a result, our model exhibits improved network adaptability and superior performance in the LIE task compared to other algorithms. © 2023 IEEE.

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Integrated Direct/Indirect Adaptive Robust Control for Electrical Driven Injection Machine Mold Closing with Accurate Parameter Estimations 16th

Integrated Direct/Indirect Adaptive Robust Control for Elec...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhu, Shiqiang Meng, Qiwei Song, Wei Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Zhejiang Lab Hangzhou Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

In this paper, the control issue of the mold closing system of Injection machine is studied. In actual application, there are two major control problems needed to be addressed in controller design: to ensure the quality of products, the precision of mold needs to be guaranteed;the operation parameters of the machines are willing to be recorded for analysis, such as health monitoring and prognosis. However, the existing controllers usually rely on the servo motor, where the dynamics of the mold closing system is not taken into consideration, that may lead to degraded tracking performances. Moreover, the uncertain model parameters are usually ignored or simply treated as disturbances. Instead in this paper, the nonlinear dynamic model of mold closing system is first proposed including parameter uncertainties and disturbances for controller design. Subsequently, an integrated direct/indirect adaptive robust control law(DIARC) is proposed that not only achieves excellent tracking performance but also attenuates the parameter uncertainties with accurate parameter learning. Comparative experiments are carried out to verify our proposed approach and the results demonstrate the effectiveness of the integrated direct/indirect adaptive robust control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robust control

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Decision-Making in Robotic Grasping with Large Language Models 16th

Decision-Making in Robotic Grasping with Large Language Mo...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhang, Haoyang Qian, Haofu Meng, Qiwei Sun, Yinan Sun, Yao Song, Wei Zhu, Shiqiang Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

Recent advances in large language models have highlighted their potential to encode massive amounts of semantic knowledge for long-term autonomous decision-making, positioning them as a promising solution for powering the cognitive capabilities of future home-assistant robots. However, while large language models can provide high-level decision, there is still no unified paradigm for integrating them with robots’ perception and low-level action. In this paper, we propose a framework centered around a large language model, integrated with visual perception and motion planning modules, to investigate the robotic grasping task. Unlike traditional methods that only focus on generating stable grasps, our proposed approach can handle personalized user instructions and perform tasks more effectively in home scenarios. Our approach integrates existing state-of-the-art models in a simple and effective way, without requiring any fine-tuning, which makes it low-cost and easy to deploy. Experiments on a physical robot system demonstrate the feasibility of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision making

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Design and Development of the Dual-Arm Robot DARO 5

Design and Development of the Dual-Arm Robot DARO

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Liu, Yun Huang, Qiulang Zhang, Pu Yan, Mindong Liang, Dingkun Kong, Lingyu Wang, Xin Xie, Anhuan Gu, Jason Zhu, Shiqiang Research Institute of Interdisciplinary Innovation Zhejiang Lab Research Center for Intelligent Robotics Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Dalhousie University Electrical and Computer Engineering HalifaxNS Canada

ISBN: (纸本)9798350307320

This paper presents the design and development of a humanoid dual-arm robot called DARO, along with its improved version, DARO-N. DARO and DARO-N consist of two arms and humanoid hands, each with 6/7 degrees of freedom for the arms and 7 active degrees of freedom for the hands. Modular commercial actuators are employed as the driving joints of the arms, allowing for reduced development time and cost. The design principles of modularity, simplicity, lightweight construction, human likeness, information perception capability, and scalability are incorporated into the design. Furthermore, this work establishes a control framework for the humanoid dual-arm robot and conducts simulations and multiple physical experiments to validate the performance of the robots. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot

TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Wen Lin, Zheyuan Huang, Min Hu, Shunda Jin, Tianlei Li, Te Wan, Minhong Gu, Jason Zhu, Shiqiang Research Center for Intelligent Robotics Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9781665481090

A service robot serving safely and politely needs to track the surrounding people robustly, especially for Tour-Guide Robot (TGR). However, existing multi-object tracking (MOT) or multi-person tracking (MPT) methods are not applicable to TGR for the following reasons: 1. lacking relevant large-scale datasets;2. lacking applicable metrics to evaluate trackers. In this work, we target the visual perceptual tasks for TGR and present the TGRDB dataset, a novel large-scale multi-person tracking dataset containing roughly 5.6 hours of annotated videos and over 450 long-term trajectories. Besides, we propose a more applicable metric to evaluate trackers using our dataset. As part of our work, we present TGRMPT, a novel MPT system that incorporates information from head shoulder and whole body, and achieves state-of-the-art performance. We have released our codes and dataset in https://***/wenwenzju/TGRMPT. © 2022 IEEE.

关键词： Large dataset

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