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检索条件"任意字段=IEEE International Symposium on Computational Intelligence in Robotics and Automation"

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ieee Joint 22nd international symposium on computational intelligence and Informatics and 8th international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics, CINTI-MACRo 2022 - Proceedings

IEEE Joint 22nd International Symposium on Computational Int...

引用

Joint 22nd ieee international symposium on computational intelligence and Informatics and 8th ieee international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics, CINTI-MACRo 2022

ISBN: (纸本)9798350398823

The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmful speech detection using machine learning;comparison of automatic question generation techniques;transparent slide detection and gripper design for slide transport by robotic arm;proposing a new model for estimation of oil rate passing through wellhead chokes in an Iranian heavy oil field;real-time multi-user 3D visualization software in medicine;evaluation of the use of an intelligent system in the calibration of a refined car-following model;inertial sensor-based movement classification with dimension reduction based on feature aggregation;and on the simulation of lower order control strategies for higher order systems.

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ieee Joint 19th international symposium on computational intelligence and Informatics and 7th international Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and robotics, CINTI-MACRo 2019 - Proceedings

IEEE Joint 19th International Symposium on Computational Int...

引用

19th ieee Joint international symposium on computational intelligence and Informatics and 7th international Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and robotics, CINTI-MACRo 2019 - Proceedings

ISBN: (纸本)9781728156255

The proceedings contain 36 papers. The topics discussed include: mixed-order Sugeno model to predict the resultant force in the milling process for honeycomb sandwich;stoichiometry control of the two gas reactive sputtering process;demand side management electric energy consumption data processing architectures within internet of things context;development of an LQ regulator for longitudinal vehicle control of an automated vehicle;parking lot exploration strategy;a semi-automated generation of entity relationship diagram based on morphosyntactic tagging from the requirements written in a Serbian natural language;data analytics for health-care risk predictions based on ensemble classifiers and subjective projection;and data analytics for health-care risk predictions based on ensemble classifiers and subjective projection.

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Real-time Dynamic-consistent Motion Planning for Over-actuated UAVs

Real-time Dynamic-consistent Motion Planning for Over-actuat...

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ieee international Conference on robotics and automation (ICRA)

作者： Su, Yao Zhang, Jingwen Jiao, Ziyuan Li, Hang Wang, Meng Liu, Hangxin Beijing Inst Gen Artificial Intelligence BIGAI Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Existing motion planning approaches for over-actuated unmanned aerial vehicle (UAV) platforms can achieve online planning without considering dynamics. However, in many envisioned application areas such as aerial manipulation, payload delivery, and moving target tracking, it is critical to ensure dynamic consistency in the generated trajectory. The dynamics of these platforms introduce a high nonlinearity, leading to a substantial increase in computational burden. This paper presents an efficient method to plan motions that are consistent with the dynamics of over-actuated UAVs. With a hierarchical control structure, the dimension of the optimization problem is greatly reduced with synthesized wrench commands. Additionally, by exploring the dynamics of over-actuated UAVs, the complex planning process is decoupled into two simpler sub-problems. As a result, the proposed planner can be solved as two small quadratic programmings (QPs) and deployed in realtime. The computational efficiency and dynamic consistency of the proposed method are verified through both simulations and experiments, including comparison with other approaches and dynamic target tracking.

关键词： Unmanned aerial vehicles (UAV)

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Conflict Area Prediction for Boosting Search-Based Multi-Agent Pathfinding Algorithms

Conflict Area Prediction for Boosting Search-Based Multi-Age...

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ieee international Conference on robotics and automation (ICRA)

作者： Ryu, Jaesung Kwon, Youngjoon Yoon, Sangho Lee, Kyungjae Chung Ang Univ Dept Artificial Intelligence Seoul 06974 South Korea

ISBN: (纸本)9798350384581;9798350384574

We address the challenge of efficiently controlling multi-agent systems, crucial in fields like logistics and traffic management. We propose a novel approach that combines learning-based techniques with search-based methods, focusing on enhancing the conflict-based search (CBS). The CBS ensures optimality but suffers from increasing complexity as agents or maps grow. To tackle this, we leverage learning-based approaches to enhance computational efficiency. By training a conflict area prediction (CAP) network, we anticipate potential conflict areas, allowing for low-level path planners to explore conflict-free paths. Our experiments demonstrate the effectiveness of our method in reducing computational demands compared to existing approaches.

关键词： Multi agent systems

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Distributional Reinforcement Learning with Sample-set Bellman Update

Distributional Reinforcement Learning with Sample-set Bellma...

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ieee international Conference on robotics and automation (ICRA)

作者： Zhang, Weijian Wang, Jianshu Yu, Yang Polixir Ai Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Natl Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Distributional Reinforcement Learning (DRL) not only endeavors to optimize expected returns, but also strives to accurately characterize the full distribution of these returns, a key aspect in enhancing risk-aware decision-making. Previous DRL implementations often inappropriately treat statistical estimations as concrete samples, which undermines the integrity of learning. While several studies have addressed this issue, they frequently give rise to new complications, including computational burdens and diminished stochastic behavior. In our work, we present a novel DRL framework that leverages the Gaussian mixture model to adeptly depict the distribution of returns. This approach ensures precise, authentic sampling critical for robust learning, while also preserving computational tractability. Through extensive evaluation on a diverse array of 59 Atari games, our method not only surpasses the efficacy of prior DRL algorithms but also presents formidable competition to contemporary top-tier RL algorithms, signifying a substantial advancement in the field.

关键词： Adversarial machine learning

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ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

ASPIRe: An Informative Trajectory Planner with Mutual Inform...

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ieee international Conference on robotics and automation (ICRA)

作者： Zhou, Kangjie Wu, Pengying Su, Yao Gao, Hao Ma, Ji Liu, Hangxin Liu, Chang Peking Univ Coll Engn Dept Adv Mfg & Robot Beijing Peoples R China Beijing Inst Gen Artificial Intelligence BIGAI Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

关键词： Adaptive filters

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EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization

EffLoc: Lightweight Vision Transformer for Efficient 6-DOF C...

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ieee international Conference on robotics and automation (ICRA)

作者： Xiao, Zhendong Chen, Changhao Yang, Shan Wei, Wu South China Univ Technol Sch Automat Sci & Engn Control Sci & Engn Guangzhou Guangdong Peoples R China Natl Univ Def Technol Colloge Intelligence Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving. It estimates 3D camera position and orientation (6-DoF) from images. Unlike traditional methods like SLAM, recent strides use deep learning for direct end-to-end pose estimation. We propose EffLoc, a novel efficient Vision Transformer for single-image camera relocalization. EffLoc's hierarchical layout, memory-bound self-attention, and feed-forward layers boost memory efficiency and inter-channel communication. Our introduced sequential group attention (SGA) module enhances computational efficiency by diversifying input features, reducing redundancy, and expanding model capacity. EffLoc excels in efficiency and accuracy, outperforming prior methods, such as AtLoc and MapNet. It thrives on large-scale outdoor car-driving scenario, ensuring simplicity, end-to-end trainability, and eliminating handcrafted loss functions.

关键词： Machine vision

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IMU-Aided Event-based Stereo Visual Odometry

IMU-Aided Event-based Stereo Visual Odometry

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ieee international Conference on robotics and automation (ICRA)

作者： Niu, Junkai Zhong, Sheng Zhou, Yi Hunan Univ Neuromorph Automat & Intelligence Lab NAIL Sch Robot Changsha Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Direct methods for event-based visual odometry solve the mapping and camera pose tracking sub-problems by establishing implicit data association in a way that the generative model of events is exploited. The main bottlenecks faced by state-of-the-art work in this field include the high computational complexity of mapping and the limited accuracy of tracking. In this paper, we improve our previous direct pipeline Event-based Stereo Visual Odometry in terms of accuracy and efficiency. To speed up the mapping operation, we propose an efficient strategy of edge-pixel sampling according to the local dynamics of events. The mapping performance in terms of completeness and local smoothness is also improved by combining the temporal stereo results and the static stereo results. To circumvent the degeneracy issue of camera pose tracking in recovering the yaw component of general 6-DoF motion, we introduce as a prior the gyroscope measurements via pre-integration. Experiments on publicly available datasets justify our improvement. We release our pipeline as an open-source software for future research in this field.

关键词： Mapping

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Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving

Lightning NeRF: Efficient Hybrid Scene Representation for Au...

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ieee international Conference on robotics and automation (ICRA)

作者： Cao, Junyi Li, Zhichao Wang, Naiyan Ma, Chao Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tusimple Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Recent studies have highlighted the promising application of NeRF in autonomous driving contexts. However, the complexity of outdoor environments, combined with the restricted viewpoints in driving scenarios, complicates the task of precisely reconstructing scene geometry. Such challenges often lead to diminished quality in reconstructions and extended durations for both training and rendering. To tackle these challenges, we present Lightning NeRF. It uses an efficient hybrid scene representation that effectively utilizes the geometry prior from LiDAR in autonomous driving scenarios. Lightning NeRF significantly improves the novel view synthesis performance of NeRF and reduces computational overheads. Through evaluations on real-world datasets, such as KITTI-360, Argoverse2, and our private dataset, we demonstrate that our approach not only exceeds the current state-of-the-art in novel view synthesis quality but also achieves a five-fold increase in training speed and a ten-fold improvement in rendering speed. Codes are available at https://***/VISION-SJTU/Lightning-NeRF.

关键词： Rendering (computer graphics)

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A Constrained Path Following Method for Snake-like Manipulators via Controlled Winding Uncoiling Strategy

A Constrained Path Following Method for Snake-like Manipulat...

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ieee international Conference on robotics and automation (ICRA)

作者： Lu, Mingrui Tian, Yunong Chen, Yinghua Chen, Minghao Zhang, Yanfeng Li, En Tan, Min Chinese Acad Sci Inst Automat Engn Lab Ind Vis & Intelligent Equipment Technol 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence 19 A Yuquan Rd Beijing 100049 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Benefiting from its hyper-redundant structure, the biomimetic snake-like manipulator retains its remarkable flexibility even within confined spaces. However, its motion planning and control pose significant challenges. This paper imitates the winding uncoiling behavior of snakes to achieve controllable constrained path following. Firstly, based on control points, a recursive computational model and an equivalent planning angle model are established, enabling efficient and analytical determination of joint positions, collision regions, and motion parameters during the path following. Subsequently, the sliding control point algorithm and motion smoothing restriction algorithm are designed. The former ensures that the remaining segments during following strictly remain within the collision-free regions defined by the base and path controls, while the latter smooths the control parameters based on velocity and acceleration limitations. Finally, simulation and practical experiments demonstrate the feasibility of the proposed methods. The prototype that applied our method can reach targets and accomplish tasks, further validating the applicability of the snake-like manipulator.

关键词： Redundant manipulators

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