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检索条件"任意字段=2024 International Conference on Intelligent Robotics and Automatic Control"

共 5267 条记录，以下是451-460 订阅

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Safety and Sustainability Indicators of Variable Speed Limit control in intelligent Driver Models 21

Safety and Sustainability Indicators of Variable Speed Limit...

引用

21st IEEE international conference on Smart Communities: Improving Quality of Life using AI, robotics and IoT, HONET 2024

作者： Pop, Madalin-Dorin Carbune, Viorel Sudacevschi, Viorica Politehnica University of Timişoara Department of Computer and Information Technology Timişoara Romania Technical University of Moldova Department of Computer Sciences and Systems Engineering Chişinǎu Moldova

ISBN: (纸本)9798350378078

The variable speed limit (VSL) control is responsible for real-Time adjustment of speed limits based on traffic conditions in sensor-based environments. Integration of this traffic management strategy with intelligent driver models (IDMs) enables the development of control systems for autonomous vehicles. This article identifies the indicators used for the performance evaluation of VSL strategies implemented in IDM. A classification of these indicators into safety and sustainability categories simplifies the identification of the targeted measures of effectiveness (MOEs) of a proposed VSL strategy, such as safety, mobility, or environmental sustainability. Furthermore, this article reviews existing VSL strategies developed for IDMs and discusses how their assessment was carried out based on identified indicators. © 2024 IEEE.

关键词： Traffic control

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Research on Bicycle Motion control Based on Reinforcement Learning 3

Research on Bicycle Motion Control Based on Reinforcement Le...

引用

3rd international conference on Service robotics, ICoSR 2024

作者： Deng, Hang Chen, Xiai Dong, Mingze Yang, Jongkun College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (纸本)9798350367119

This paper presents a study on the control of unmanned bicycle motion based on inertial wheels in the pybullet physics simulation environment. To address the nonlinear and strongly coupled nature of unmanned bicycles, the Proximal Policy Optimization deep reinforcement learning algorithm is employed in this study to achieve stable motion control of the bicycle. Two sets of independent reward functions are designed for balancing and path tracking of the unmanned bicycle to improve control effectiveness. Simulation results demonstrate that the designed control strategy can effectively maintain the balance of the unmanned bicycle and accurately track the desired path. This research provides a novel approach and insight into the intelligent control of unmanned bicycles, showcasing the application potential of reinforcement learning-based motion control in highly complex systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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AviaTech: Enhancing Jenday Conure Welfare Through intelligent Cage Systems

AviaTech: Enhancing Jenday Conure Welfare Through Intelligen...

引用

2024 international conference on control, robotics and Informatics, ICCRI 2024

作者： Rey, William P. Rey, Kieth Wilhelm Jan D. School of Information Technology Mapua University Makati City Philippines

ISBN: (纸本)9798350380309

The welfare of pet birds, particularly species like Jenday Conures, is essential for their health and happiness. Traditional birdcages often struggle to meet the needs of these intelligent and social birds, prompting the exploration of innovative solutions. Leveraging Internet of Things (IoT) technology, this study introduces AviaTech, an intelligent bird cage system designed specifically for Jenday Conures. AviaTech integrates mobile connectivity, environmental monitoring sensors, and automated feeding and watering mechanisms to enhance bird welfare and provide convenience for pet owners. Through a comprehensive methodology encompassing design, development, evaluation, and testing phases, AviaTech was refined to ensure effectiveness, usability, and alignment with user needs. Results demonstrate AviaTech's capability to provide secure access, real-time monitoring, remote control functionality, and valuable insights into bird behavior and habitat conditions. User satisfaction was confirmed through qualitative and quantitative evaluations, highlighting the system's high usability and acceptance. Overall, AviaTech represents a significant advancement in pet bird care, offering a user-friendly and technologically sophisticated solution to meet the evolving needs of pet owners and enhance the well-being of Jenday Conures. © 2024 IEEE.

关键词： Birds

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Model Predictive Path Integral control for Agile Unmanned Aerial Vehicles

Model Predictive Path Integral Control for Agile Unmanned Ae...

引用

2024 international conference on intelligent Robots and Systems

作者： Minarik, Michal Penicka, Robert Vonasek, Vojtech Saska, Martin Czech Tech Univ Fac Elect Engn Multirobot Syst Grp Prague Czech Republic

ISBN: (纸本)9798350377712;9798350377705

This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km h(-1) and acceleration close to 20 m s(-2), while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight.

关键词： Unmanned aerial vehicles (UAV)

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A gait control method for bipedal robots based on reward function optimization 4

A gait control method for bipedal robots based on reward fun...

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4th international conference on intelligent Communications and Computing

作者： Pei, Ruoyan Ma, Pengge Zhang, Tong Li, Lan Zhengzhou Univ Aeronaut Zhengzhou Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798331530372;9798331530365

The intelligent control of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult for robots to maintain walking stability and simulate human gait at the same time for its excessive attention on stability. Compared with the traditional deep reinforcement learning algorithm, the DDPG algorithm has the ability to handle continuous action space and high-dimensional state space, and has a wide application prospect in many practical problems. We proposed a bipedal robot control method based on reward function optimization to enable the robot to achieve both stable high-speed movement and human gait imitation. This paper combines the physical characteristics of the bipedal robot to establish a control system based on the DDPG algorithm. At the same time, the reward function is designed to guide the robot to learn the correct walking strategy. Through the comparative test, the weight limit ratio of each reward function for the bipedal robot to stably increase the speed is given. The simulation results show that the method proposed in this paper has good practicality and effectiveness.

关键词： Bipedal robot gait control deep reinforcement learning DDPG algorithm reward function

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Investigating the Use of Low-cost Tactile Sensor in Emulating Mechanoreceptor Patterns and in Hardness-Based Classification 10

Investigating the Use of Low-cost Tactile Sensor in Emulatin...

引用

10th international conference on control, Automation and Robotic (ICCAR)

作者： Sharma, Yash Ferreira, Pedro Justham, Laura Beatty, Matthew Loughborough Univ Wolfson Sch MEME Intelligent Automat Ctr Loughborough England

ISBN: (纸本)9798350373172;9798350373189

Tactile sensors and material classification are important aspects of improving robotics grasping. Previously many different tactile sensors and classification have been introduced. Tactile sensors used were either customized in different arrays or have complex architecture. Artificial mechanoreceptor type sensors have also been used to perform different types of classification, but hardness-based classification has never been investigated. In some cases, general tactile sensors were used to perform hardness-based classification, but it didn't show up good accuracy score while using different machine learning algorithms. This approach uses off shelf tactile sensors and emulates mechanoreceptor patterns using neural-spike encoder techniques to represent that off shelf tactile sensors have capability to emulate spike train which may indicate some patterns same as human receptors. And further using raw sensor data and digital spike patterns data as additional feature to performs analysis. Outcome of hardness-based classification with 20% test size data indicates that spike patterns data can increase predictability accuracy score in different combination of sensors data where with three sensors it remains highest of 93.9% improved from 91.8%. And spike patterns for some mechanoreceptors also showcase that off shelf can be used to generate mechanoreceptor patterns.

关键词： Tactile Sensors Mechanoreceptors Spike Patterns Machine Learning Hardness Classification Spike Coding

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IRAC 2024 Copyright Page

IRAC 2024 Copyright Page

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intelligent robotics and automatic control (IRAC), international conference on

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Trajectory Tracking control for Quadrotor UAVs with Variable Mass Load Based on Extended State Observer 7

Trajectory Tracking Control for Quadrotor UAVs with Variable...

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7th international conference on intelligent robotics and control Engineering, IRCE 2024

作者： Yang, Donghao Wang, Bing Bian, Shiyao Wang, Wancheng College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (纸本)9798350352399

The inherent underactuation and strong coupling characteristics of quadrotor UAVs make the design of their trajectory tracking controllers challenging. To address the issues of dynamic uncertainties and unknown external disturbances in quadrotor UAVs, this paper designs a trajectory tracking controller for quadrotor UAVs with variable mass loads based on the inner-outer loop control structure. An adaptive sliding mode controller based on an extended state observer is used in the outer loop to adaptively estimate the mass of the quadrotor UAV with loads and achieve trajectory tracking. In the inner loop, a composite fast nonsingular terminal sliding mode controller based on an extended state observer is employed to ensure stable tracking of the desired attitude angles. Simulation results demonstrate that the proposed control scheme ensures high-precision trajectory tracking of the quadrotor UAV under variable load mass and disturbances, indicating the effectiveness and robustness of the controller. © 2024 IEEE.

关键词： Sliding mode control

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Requirement Mining from Closed-Loop control Models via Human-Computer Collaboration 4

Requirement Mining from Closed-Loop Control Models via Human...

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4th international conference on Computer, control and robotics (ICCCR)

作者： Lu, Penghong Chen, Gang South China Univ Technol ShienMing Wu Sch Intelligent Engn Guangzhou Peoples R China

ISBN: (纸本)9798350373141;9798350373158

This study introduces an innovative human-computer collaboration framework for mining requirements from closed-loop control models in industrial systems. Traditional approaches in this domain face challenges due to vague, non-modular, evolving, or sometimes unknown requirements. Our framework bridges this gap by enabling a synergetic interaction between the user and a computer agent, facilitating the efficient mining of optimal specifications through an interactive interface. We conducted two experimental studies with ten participants to validate our approach. The findings reveal that the integration of human expertise and computational algorithms accelerates the optimization process, thereby aiding designers in achieving satisfactory system designs more rapidly. This research contributes to the field of model-based system design by presenting a novel paradigm that leverages the strengths of both human intuition and computer-based optimization.

关键词： Human-computer collaboration model-based design requirement mining signal temporal logic

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A Hierarchical Framework for Cooperative Tasks in Multi-agent Systems 11

A Hierarchical Framework for Cooperative Tasks in Multi-agen...

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11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on robotics, Automation and Mechatronics (RAM)

作者： Zhu, Yuanning Yang, Qingkai Tian, Daiying Fang, Hao Beijing Inst Technol Sch Automat Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350364200;9798350364194

In this paper, we propose a hierarchical framework for multi-agent systems to enhance cooperative tasks in dynamic environments. Accomplishing cooperative tasks can be challenging in dynamic environments. Reinforcement learning is a popular approach in this field, enabling agents to make real-time decisions. However, large state and action spaces often lead to poor performance, such as slow convergence and suboptimal policies. To address this issue, we utilize a hierarchical framework. Long-horizon and complicated tasks are decomposed into multiple subtasks. At the low-level, each subtask has a corresponding decision-making model, trained using the Soft Actor-Critic reinforcement learning algorithm. Additionally, a high-level component is introduced to determine which subtask to tackle at any given time. We discuss our method in the context of the popular hunting problem involving pursuers and an evader. Simulation demonstrates the efficacy and feasibility of our method in the hunting problem environment setting.

关键词： cooperative systems and control multi-agent systems deep reinforcement learning

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