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检索条件"主题词=Robot Programming"

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Improved DQN based inspection robot obstacle avoidance path planning 3

Improved DQN based inspection robot obstacle avoidance path ...

引用

3rd International Conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Yang, Ting Li, Xu School of Intelligent Manufacturing Chengdu Technological University Chengdu611730 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

To address issues such as long learning time, poor exploration ability, over-reliance on prior environmental information, and lack of real-time response in obstacle avoidance for inspection robots under conventional path planning methods, an improved DQN-based obstacle avoidance path planning algorithm is proposed. This algorithm combines priority strategies, dynamic exploration factors, and an optimized reward function to avoid repetitive learning of the same obstacles, aiming to improve the robot's path planning efficiency and success rate. The optimized reward function balances path length and obstacle avoidance effectiveness, avoiding local optima and blind search. The designed dynamic exploration factor function ensures that later training emphasizes utilizing the action strategies generated by reinforcement learning. Experimental simulation results show that the improved DQN achieves higher obstacle avoidance success rates and shorter path lengths compared to traditional algorithms in environments with varying levels of complexity, providing an efficient solution for obstacle avoidance path planning of inspection robots in complex dynamic environments. © 2025 SPIE.

关键词： robot programming

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A Task Planning Method Combining Physical Therapy Experience for Physical Therapy Massage robots 2nd

A Task Planning Method Combining Physical Therapy Experience...

引用

2nd International Conference on the Frontiers of robotics and Software Engineering, FRSE 2024

作者： Yuan, Xin Ding, Jiahui Zhao, Donghui Niu, Chunhui Sun, Xingwang Yang, Junyou Shenyang University of Technology Shenyang110870 China Jianghuai Advance Technology Center Hefei230000 China

ISBN: (纸本)9789819630127

The demand for massages is rising daily in the modern day due to population aging. The creation of an intelligent physicaltherapy massage robot is currently a pressing task due to the severe scarcity of qualified masseurs in the field of physical therapy massage. In this paper, we use the back therapeutic massage robot as our research subject, build the system’s framework, and conduct a thorough analysis of the therapeutic massage operation’s task planning technique using customized features. While there are many different types of physical therapy robots on the market, the majority of them are limited to basic massage tasks and do not take the patient’s psychological state into account. They also have issues with basic trajectories. This research proposes a new task planning method for robotic physical therapy massage that uses a depth camera to identify the patient’s back acupoints, which greatly increases the safety and comfort of back massage. To achieve effective massage task planning, the method combines linear interpolation in Cartesian space with fifth-degree polynomial interpolation. The results of simulations and real-world experiments demonstrate that the suggested approach has great generality and operability, fully accounting for patient individual differences and psychological comfort in addition to having smooth and stable qualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： robot programming

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RoboCodeX: Multimodal Code Generation for robotic Behavior Synthesis 41

RoboCodeX: Multimodal Code Generation for Robotic Behavior S...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Mu, Yao Chen, Junting Zhang, Qinglong Chen, Shoufa Yu, Qiaojun Ge, Chongjian Chen, Runjian Liang, Zhixuan Hu, Mengkang Tao, Chaofan Sun, Peize Yu, Haibao Yang, Chao Shao, Wenqi Wang, Wenhai Dai, Jifeng Qiao, Yu Ding, Mingyu Luo, Ping Department of Computer Science The University of Hong Kong Hong Kong OpenGVLab Shanghai AI Laboratory China ETH Zurich Switzerland Shanghai Jiao Tong University China The Chinese University of Hong Kong Hong Kong Tsinghua University China UC Berkely United States

robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successes in applying multimodal large language models for high-level understanding, it remains challenging to translate these conceptual understandings into detailed robotic actions while achieving generalization across various scenarios. In this paper, we propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints, and applies code generation to introduce generalization ability across various robotics platforms. To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative refining methodology is introduced for supervised fine-tuning. Extensive experiments demonstrate that RoboCodeX achieves state-of-the-art performance in both simulators and real robots on four different kinds of manipulation tasks and one embodied navigation task. More demos and information can be found in our homepage. Copyright 2024 by the author(s)

关键词： robot programming

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Optimal Trajectory Planning for robotic Arm Based on Improved Whale Optimization Algorithm 3

Optimal Trajectory Planning for Robotic Arm Based on Improve...

引用

3rd International Conference on Service robotics, ICoSR 2024

作者： Ni, Zhiqiang Wang, Guirong Ye, Hengchu College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (纸本)9798350367119

An integrated optimal trajectory planning approach is suggested to increase the shock resilience and operational efficiency of industrial robots. First, the refractive reverse learning mechanism, Lévy flights, and nonlinear convergence factor are incorporated to the basic whale optimization algorithm to create the improved whale optimization algorithm (IWOA). Then the superiority and efficacy of the improved algorithm are confirmed using the CEC2017 test function set. Subsequently, trajectory planning in the robot joint space utilizing 3-5-3 polynomial functions determines the whole optimization objective function in terms of time, impact, and energy consumption. When the joint motion curves derived from the improved algorithm are continuous and devoid of sudden shifts, the efficacy of the trajectory planning approach has finally been verified. © 2024 IEEE.

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Efficient Collision-Free Dual-Stage-BSpline Path Planning For Mobile robot 22

Efficient Collision-Free Dual-Stage-BSpline Path Planning Fo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhan, TengDa Wu, YiFei School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331527471

Obstacle avoidance in dense obstacle environments is difficult to be both efficient and effective owing to the characterics of the nonlinear optimization problems. This article proposed an Efficient Collision-Free Dual-Stage-BSpline Path Planning method for mobile robots. The method enhances planning efficiency by incorporating traditional A* and BSpline algorithms. Subsequently, it improves planning feasibility by introducing a multi-objective optimization term with curvature and robot kinematics constraints. Simulation results show that the proposed method could output suitable collision-free paths for differential wheeled robot. Comparing to TEB and DWA algorithms, the method proposed in this paper can combine both efficiency and smoothness in complex indoor scenary. © 2024 IEEE.

关键词： robot programming

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Dynamic Path Planning of the robotic Surgical Needle

Dynamic Path Planning of the Robotic Surgical Needle

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Shah, Jyoti Nain, Pawan Kumar Singh Johri, Prashant Sharma, Lakshmi Galgotias University Greater Noida201307 India School of Mechanical Engineering Galgotias University Uttar Pradesh India School of Computer Science Engineering Galgotias University Uttar Pradesh India

ISBN: (纸本)9798350382693

robotics surgery is fast becoming ubiquitous in medical practices from cancer treatment to prostrate treatment to neurological problems. One of the major problem associated with this invasive surgery is how to minimize the loss of the adjacent tissues and accuracy of the reaching the target. In the present paper we have presented our work related to bevel tip surgical flexible needle supported by multi degree of freedom robot. We have modelled the mathematical equation and solved it using evolutionary optimization technique, Firefly Algorithm. Our aim is to minimize the losses to the adjacent tissues with high accuracy of reaching target. From the robot a probe and robotic needle is attached that traversed the curvilinear path. Probabilistic random tree method based method is applied to reach the lesion starting from hole in the skill. Initial and final point, target, is feed as a target. The constrained with risk map is defined that must be avoided to minimizing the loss of the sensitive organs. Finally the whole problem is reduced to optimization problem with given constant. A gelatin based material is used to mimic the brain tissue with the help of this indentation by needle is shown to find the optimized path. © 2024 IEEE.

关键词： robot programming

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Control of robots Using Augmented Reality for Educational Teaching Processes 1

引用

International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Salazar, Franklin Viteri Ojeda, Jimena Catalina Jácome, Javier Albuja Amaguaña, Yesenia D. Constante Paredes, Katherine Abigail Universidad Técnica de Ambato Ambato180103 Ecuador Escuela Superior Politécnica de Chimborazo Riobamba Ecuador Instituto Tecnológico Superior Riobamba Riobamba Ecuador Universidad Autónoma de Madrid Madrid Spain

ISBN: (数字)9783031709814

ISBN: (纸本)9783031709807

In 1957, Morton Heilig invented the Sensorama, the predecessor of augmented reality (AR), which offered 3D images, sound, and sensory effects. Ivan Sutherland created the first AR system in 1966, while Myron Krueger introduced interactive AR in 1973. The term "augmented reality" emerged in 1992 and the first mobile AR system was presented in 1997. The evolution continued with applications like AR Wikitude and Google Glass glasses. AR was applied in education, events, and robotic assistance. The field of human-robot collaboration (HRC) has been developed, improving productivity and safety in industry and construction. programming and robotic simulation tools are described. The text presents a case study on AR in robotic education, describing how Unity and Vuforia were used to interact with an ABB IRB 120 robot. The programming of scenes, scripts, and the simulation of Denavit-Hartenberg parameters for robot kinematics are discussed. It shows how AR contributes to interactive learning and understanding of robotics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： robot programming

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Learning Hierarchical robot Skills Represented by Behavior Trees from Natural Language 29th

Learning Hierarchical Robot Skills Represented by Behavior ...

引用

29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： Wang, Kaiyi Zhao, Yongjia Dai, Shuling Yang, Minghao He, Yichen Zhang, Ning State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Jiangxi Research Institute Beihang University Jiangxi China Institute of Automation Chinese Academy of Sciences Beijing China Institute of Intelligent Information Processing Beihang University Beijing China

ISBN: (纸本)9783031468452

Learning from natural language is a programming-free and user friendly teaching method that allows users without programming knowledge or demonstration capabilities to instruct robots, which has great value in industry and daily life. The manipulation skills of robots are often hierarchical skills composed of low-level primitive skills, so they can be conveniently represented by behavior trees (BTs). Based on this idea, we propose NL2BT, a framework for generating behavior trees from natural language and controlling robots to complete hierarchical tasks in real time. The framework consists of two language processing stages, an initial behavior tree library composed of primitive skill subtrees, and a BT-Generation algorithm. To validate the effectiveness of NL2BT, we use it to build a Chinese natural language system for instructing robots in performing 3C assembly tasks, which is a significant application of Industry 4.0. We also discuss the positive impact of real-time teaching, visual student models, and the synonymous skill module in the framework. In addition to the demonstrated application, NL2BT can be easily migrated to other languages and hierarchical task learning scenarios. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： robot programming

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rA*: Re-Planned A∗ Technique for Point-to-Point robot Navigation in Dynamic Environments 10

rA*: Re-Planned A∗ Technique for Point-to-Point Robot Navig...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Ganguly, Tanudeep Chaudhuri, Rapti Deb, Suman NIT Agartala CSE Dept. India

ISBN: (纸本)9798331533229

Autonomous point-to-point robots employ path-finding algorithms to traverse from the start to the goal point by avoiding obstacles. One such prominent and efficient algorithm is A*. It uses the best first technique to traverse a particular environment and extract the shortest path by avoiding on-path obstacles. It is widely accepted for its optimality in finding the shortest path from one node to another. In the case of real-world scenarios, robot navigation involves dynamic obstacles with varying costs and multiple objectives. This paper proposes the rA∗ (replanned A*) technique, which implements important modifications to the A∗ algorithm to address the challenges created in dynamic environments by introducing dynamic cost adjustment with sensor data to reduce path distance in replanning in case of obstacles or complex terrain features differences in an environment. We also enhance the heuristic approach with environmental awareness by equipping Euclidean distance for faster navigation. Real-time and multi-objective path planning have been conceptualized to dynamically adjust path in case of moving obstacles. Real-time analysis of the algorithm is conducted on a Turtlebot by taking the sensor-fused input data in coordinate and depth format. The sensor data is obtained through ROS (robot Operating System) visualizer and the map of the path exploration by detecting the dynamic obstacles is generated through G-mapping. The maps are fed as input to the rA∗ algorithm to re-plan the path, distorted by the sudden appearance of dynamic obstacles. rA∗ reduces the overall and near-obstacles turning angle of the robot by 80.4% - 82.2%, as compared to the traditional A∗ technique. The results prove rA∗ to be superseded in terms of accurate path tracking and control by precise obstacle avoidance and achieving intelligent navigation. © 2024 IEEE.

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MONITORING METHODS FOR METAREASONING APPROACHES FOR RECOVERING FROM PATH PLANNING FAILURES ON MOBILE GROUND robotS

MONITORING METHODS FOR METAREASONING APPROACHES FOR RECOVERI...

引用

ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2024

作者： Molnar, Sidney L. Herrmann, Jeffrey W. Institute of Systems Research Department of Mechanical Engineering University of Maryland College ParkMD20742 United States Department of Mechanical Engineering The Catholic University of America Washington20064 United States

ISBN: (纸本)9780791888414

The planning algorithms that autonomous systems use sometimes fail to generate effective solutions, which prevents mission success. Metareasoning, which is "thinking about thinking," is one way to mitigate planning failures. To give an autonomous robot the ability to recover from these failures, we implemented a novel metareasoning approach in the ARL ground autonomy stack known as Phoenix. The metareasoning approach controls the global and local path planners in that stack by stopping and restarting the associated ROS nodes. In any metareasoning policy, there are methods of control and methods of monitoring. This study investigates the implementation of a meta-level that uses select monitoring methods that identify path planning failures for autonomous ground robots. The two monitoring methods proposed are the fixed radius methods, which are based on the mission goals, and the expected progress methods, which are based on the robot's movement capabilities. In this study, we evaluated how using these methods affected navigation performance for the path planning algorithms that are in the ARL ground autonomy stack. The results of our simulation study showed that some policies helped the robot recover from path planning problems, and the robot could get closer to its final goal. In other cases, however, the metareasoning policies disrupted the robot's navigation process and degraded performance. Additional research is needed to understand when metareasoning policies can improve performance. Copyright © 2024 by ASME.

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