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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5310 条记录，以下是121-130 订阅

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Transfer-robot task allocation algorithm considering production priority for flexible job-shop scheduling problem 18

Transfer-robot task allocation algorithm considering product...

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18th international conference on control automation robotics and vision

作者： Zhou, Meng Wang, Xinheng Liang, Zhongxing Wang, Chang Wang, Jing North China Univ Technol Coll Elect & Control Engn Beijing 100144 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 10217 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

this paper proposes a flexible job-shop scheduling problem optimization method, which focuses on providing solutions for industrial production. First, in terms of model construction, the method further considers the cost of automated guided vehicles and the priority of workpiece production based on previous methods. then, this method solves the model by non-dominated sorting genetic algorithm with self-cross and delete-mutation. It reduces the production time and energy by an average of 6.4% and 19.4%, which are 5.4% and 15.1% with the priority. Finally, the simulation verifies that the method effectively reduces the production cost while realizing the adjustment of the automated guided vehicle number and the workpiece production sequence.

关键词： Job-shop scheduling non-dominated sorting genetic algorithm priority task allocation

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Biomimetic Convergence-based High resolution Depth Estimation 18

Biomimetic Convergence-based High resolution Depth Estimatio...

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18th international conference on control automation robotics and vision

作者： Harsha, Achintya Jonna, Prashanth Nitheezkant, R. Rao, Madhav IIIT Bangalore Bangalore Karnataka India

ISBN: (纸本)9798331518509;9798331518493

Accurate depth estimation is a fundamental challenge in robotics and computer vision, crucial for applications ranging from autonomous navigation to complex object manipulation. While traditional techniques such as RGBD cameras and 3D Lidar systems provide high precision, their high costs and computational demands restrict their use in many scenarios. Conversely, monocular depth estimation (MDE) offers a more affordable solution but often struggles with depth accuracy and real-time performance. In response, this research introduces a novel convergence-based stereo vision system that leverages two independently movable cameras for dynamic depth estimation. the system facilitates depth tracking of any selected object by adapting camera orientation and extrinsic matrices to achieve millimeter-level accuracy with less than 1% error in depth estimation over 5 meters, which is significantly better than few state-of-the-art Stereo systems that offer up to 2% error over a similar range. ArUco tag-based calibration mechanism was used to improve accuracy due to precise angle calculation. Further, the system successfully tracks and localizes multiple human body joints simultaneously using the Google Mediapipe Joint Detection model, providing a 3D approximation of the subject's position concerning the system in real-time. this approach not only offers a cost-effective and precise solution for real-time depth perception but also opens new avenues for dynamic, high-resolution environmental interaction in robotics.

关键词： robotics

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control System Autonomy Improvement: An Attempt to Introduce Meta-heuristic Algorithms into Closed-loop UAV control Systems 18

Control System Autonomy Improvement: An Attempt to Introduce...

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18th international conference on control automation robotics and vision

作者： Dong, Shuyan Das, Saptarshi thornton, Alex Townley, Stuart Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn Campus Penryn TR10 9FE Cornwall England Univ Exeter Fac Environm Sci & Econ Ctr Ecol & Conservat Penryn Campus Penryn TR10 9FE Cornwall England

ISBN: (纸本)9798331518509;9798331518493

this study proposes integrating Genetic Algorithms (GAs) into control systems to enhance autonomy, particularly for unmanned aerial vehicle (UAV) operations. Traditional control systems, which rely on expert knowledge and complex mathematical calculations, limit autonomy. In contrast, GAs offer robust global search capabilities, helping to avoid local optima and enhancing computational efficiency through parallel processing. Utilizing a modified Nonlinear Auto-Regressive eXogenous (NARX) model with feedback regulation ensures system stability and accurate tracking of target values, allowing the system to learn dynamic relationships essential for control in complex nonlinear conditions. We introduce a new GA-NARX based autonomous UAV control system designed for exploration in unfamiliar environments. Our enhanced system features a self-optimizing control mechanism that enables global optimization for peak performance. this advanced control system minimizes human-machine interaction by leveraging GAs' predictive abilities to anticipate future states while significantly improving the control precision. Overall, the design of this autonomous control system aims to optimize coordination and control strategies for UAV swarms, offering innovative solutions for efficient flight patterns.

关键词： Unmanned aerial vehicles (UAV)

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Constraint-Following Based Adaptive Robust control for Underactuated Mechanical Systems 18

Constraint-Following Based Adaptive Robust Control for Under...

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18th international conference on control automation robotics and vision

作者： Wei, Cui Chen, Ye-Hwa Wei, Yanling Nanjing Tech Univ Inst Intelligent Mfg Nanjing 210009 Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

this paper introduces an adaptive robust control approach tailored for underactuated mechanical systems encountering matched and mismatched uncertainty, employing a constraint-following methodology. the control strategy unfolds in two phases: initially, a nominal control scheme is devised neglecting uncertainty and deviations in initial conditions from constraints. Subsequently, uncertainty is categorized into matched and mismatched components, ensuring that mismatched uncertainties remain unobservable. Leveraging the structural characteristics of the uncertainty bound, a novel segmented adaptive law is proposed and seamlessly integrated into the adaptive robust control framework. By employing the Lyapunov minimax approach, the method ensures uniform boundedness and uniform ultimate boundedness simultaneously, thereby ensuring approximate adherence to constraints for underactuated mechanical systems facing both matched and mismatched uncertainties alongside initial condition deviations.

关键词： Robust control

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RTPEIR: A Reverse Trajectory Prediction Enhanced Intent Recognition Algorithm for Multi-Agent Systems 18

RTPEIR: A Reverse Trajectory Prediction Enhanced Intent Reco...

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18th international conference on control automation robotics and vision

作者： Cai, Jun-Yang Liu, Qin Xu, Xin-Yu Lu, Kelin Chen, Yang-Yang Southeast Univ Sch Automat Nanjing Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

this paper proposes a novel Reverse Trajectory Prediction Enhanced Intent Recognition Algorithm (RTPEIR) that utilizes historical data to predict earlier trajectories and enhance intent recognition accuracy. the RTPEIR algorithm incorporates two main enhancements. Firstly, it leverages a combination of past and present trajectory data to reconstruct previous agent states, thereby providing a deeper understanding of agents' strategic developments. Secondly, it integrates these reconstructed trajectories with ongoing strategy recognition processes, significantly refining the predictive accuracy. Simulation experiments conducted on the StarCraft Multi-Agent Challenge (SMAC) platform show that RTPEIR outperforms existing forward trajectory prediction models by 6.5%, and existing LSTM and GRU models by 13.3% and 14.4%, respectively, in terms of intent recognition accuracy. Furthermore, when combined with deep reinforcement learning algorithms, RTPEIR demonstrates a notable improvement in win rates, highlighting its effectiveness in complex multi-agent environments.

关键词： Reverse trajectory prediction Intent recognition Multi-agent reinforcement learning

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Multivariable Dynamic Performance Seeking control of Civil Turbofan Engine 18

Multivariable Dynamic Performance Seeking Control of Civil T...

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18th international conference on control automation robotics and vision

作者： Peng, Kai Zhang, Zhaorong Wang, Hongxia Zhang, Huanshui Northwestern Polytech Univ Sch Power & Energy Xian Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9798331518509;9798331518493

As regards gas turbine engine control, how to achieve the optimal performance of engine within multiple control and physical constraints in real time is a sticky problem to be solved. A novel multivariable optimal control architecture is proposed to compensate engine into a pseudo-linear system by using the nonlinear dynamic inverse and the constrained optimization, and then linear feedback controller is used for control synthesis. At each sampling instant, a real-time linearized model of engine is applied to reduce computational complexity. the simulation results show the resultant control system of civil turbofan engine has good decoupling and tracking response.

关键词： Gas turbines

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NMPC for Trajectory Tracking of Hybird Terrestrial-Aerial Vehicles with Collision Avoidance 18

NMPC for Trajectory Tracking of Hybird Terrestrial-Aerial Ve...

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18th international conference on control automation robotics and vision

作者： Wang, Haoyu Miao, Zhiqiang Tang, Haoming Chen, Yizong Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Percept & Control Changsha Peoples R China

ISBN: (纸本)9798331518509;9798331518493

In recent years, researches on Hybrid Terrestrial-Aerial Vehicles (HTAVs) have received a lot of attention. However, most of the existing researches focus on achieving basic motion control in both modes, thus neglecting the situation of encountering obstacles during motion. To fill this research gap and achieve accurate trajectory tracking with collision avoidance, we proposes the use of Nonlinear Model Predictive control (NMPC). In this paper, we firstly focus on passive-wheeled HTAVs as the target platform. Subsequently, we introduce distance constraint function and dynamics models for both aerial and terrestrial modes, along with three motion constraints specific to the terrestrial mode. then we designed the NMPC controller base on distance constraint function and the dynamics models for each mode, incorporating the motion constraint in the terrestrial controller to ensure smooth movement on the ground. At the end of this paper, we conduct simulation experiments to evaluate the effectiveness of trajectory tracking. the results of simulations demonstrate that regardless of the mode, the HTAV achieves a relatively high tracking accuracy and avoid collision when following a nonlinear trajectory with fixed initial position and orientation. Additionally, the position error converges rapidly and exhibits minimal fluctuation, highlighting the significant role played by the added constraints in control.

关键词： Predictive control systems

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Position control of Cart-Driven Inverted Pendulum by Visual Feedback controller Using Non-Calibrated Images Taken through Fisheye Lens 18

Position Control of Cart-Driven Inverted Pendulum by Visual ...

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18th international conference on control automation robotics and vision

作者： Sato, Masayuki Hatada, Kazuyoshi Otomo, Shohei Kumamoto Univ Fac Adv Sci & Technol Kumamoto 8608555 Japan Fukuoka Univ Dept Elect Engn Fukuoka 8140180 Japan Kumamoto Univ Fac Engn Kumamoto 8608555 Japan

ISBN: (纸本)9798331518509;9798331518493

this paper addresses the position control of a cart-driven inverted pendulum, whose position and angle are both calculated from the non-calibrated images taken through a fisheye lens, using a visual feedback controller. Fisheye lenses, which have the ability to obtain 180 degrees wide or even wider view images, are advantageous to measurement compared to normal lenses;however, large distortion of the images cannot be avoided. In other words, fisheye lenses have a good potential as a measurement apparatus thanks to the wide view angles and the easy implementability as an add-on apparatus, it is necessary to overcome the drawback of the large image distortion to enhance the applicability. When fisheye lenses are used for controlling some systems, one of the possible solutions to overcome the drawback is to make the controllers robust against the image distortion. Along with this approach, we have already designed a visual stabilizing feedback controller for a cart-driven inverted pendulum and have confirmed its robustness against the large image distortion;however, cart position control has not yet been addressed. In this paper, we tackle the cart position control by designing a visual servo feedback controller within the framework of the conventional scaled-H-infinity problem, examine control performance by experiments conducted in the laboratory, and confirm the usefulness of fisheye lenses.

关键词： Inverted pendulum

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A Distributed Multi-Robot Collaborative Hunting Method in Dynamic Cluttered Environments 18

A Distributed Multi-Robot Collaborative Hunting Method in Dy...

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18th international conference on control automation robotics and vision

作者： Wang, Chang Li, Jianyu Zhou, Meng Zhang, Lin Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 10217 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China North China Univ Technol Coll Elect & Control Engn Beijing 100144 Peoples R China Chongqing Jiaotong Univ Coll Shipping & Naval Architecture Chongqing 400074 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

the paper proposes a distributed multi-robot cooperative hunting method in cluttered environments with guaranteed collision avoidance. First, to achieve collision avoidance, a robot safety region collision avoidance method based on Buffered Voronoi Cells (BVC) is proposed. then, under the BVC collision avoidance framework, a Kalman filer is designed to estimate the evader' position. Next, a hunting cost function based on the evader's position probability density function is constructed. Furthermore, the hunting controller for the pursuers is designed inspired by Voronoi coverage control. Finally, simulation results demonstrate that under the influence of this controller, the distance between the pursuers and the evader continuously decreases until the hunting is successful.

关键词： Multi-robot cooperative hunting collision avoidance buffered voronoi cell coverage control

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Probabilistic Multi-Robot Collision Avoidance Online Path Planning Method using an Improved Sine-cosine Algorithm 18

Probabilistic Multi-Robot Collision Avoidance Online Path Pl...

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18th international conference on control automation robotics and vision

作者： Zhou, Meng Li, Jianyu Wang, Chang Wang, Jing North China Univ Technol Coll Elect & Control Engn Beijing 100144 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 10217 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

this paper proposes an online path planning method for multi-robot collision avoidance that accounts for uncertainties in robot positioning. Firstly, the online path planning problem for multiple robots is transformed into a multi-objective optimization problem by considering some necessary factors that affect task efficiency and safety. Notably, the paper considers the uncertainty in robot positions, in which the position follows a Gaussian distribution. then, by converting probabilistic collision conditions into deterministic constraints, the fitness function ensuring probabilistic collision avoidance is constructed. Next, an improved sine-cosine algorithm is proposed to solve the ideal position. Finally, simulation experiments demonstrate that the proposed method effectively resolves collision issues under probabilistic uncertainty conditions.

关键词： Multi-robot online path planning position uncertainty probabilistic collision avoidance improved sin-cosin algorithm

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