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检索条件"主题词=Robot Programming"

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Efficient Path Planning and Collision Avoidance for Autonomous robot Navigation 21

Efficient Path Planning and Collision Avoidance for Autonomo...

引用

21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Gul, Kaynat Ul Islam, Naeem Department of Electrical Engineering Islamabad Pakistan Taoyuan32003 Taiwan

ISBN: (纸本)9798331516680

The motion planning problem is a fundamental challenge in the field of robotics. This study presents a novel sampling-based pathfinding algorithm that enables robot to navigate complicated terrain by providing obstacle avoidance and local trajectory planning. Sampling-based pathfinding algorithms have been extensively utilized in motion planning challenges. The method has several limitations, including a sluggish convergence rate, significant variation in search time, a vast and dense sample space, and non-smooth search routes. This study introduces RE-RRT∗ (Robust and Efficient RRT*), an enhanced version of the sampling-based pathfinding algorithm designed to efficiently discover a rapid path that is nearly optimal. RE-RRT∗ iteratively improves the path by utilizing the Choose Parent and Rewire processes. As the random tree expands, the sample space becomes restricted, resulting in a reduction in unnecessary searches. Simulation demonstrate that the RE-RRT* algorithm is capable of achieving smoother and more efficient convergence to a shorter path with fewer iterations, even in the presence of various obstacles. The proposed approach can enhance search efficacy, expedite convergence, and reduce processing time. Due to these factors, our proposed RE-RRT* algorithm has superior performance compared to previous sample-based algorithms in terms of stability, speed, computing time, and sampling space. © 2024 IEEE.

关键词： robot programming

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Autonomous Interactive Correction MLLM for Robust robotic Manipulation 8

Autonomous Interactive Correction MLLM for Robust Robotic Ma...

引用

8th Conference on robot Learning, CoRL 2024

作者： Xiong, Chuyan Shen, Chengyu Li, Xiaoqi Zhou, Kaichen Liu, Jeremy Wang, Ruiping Dong, Hao School of Computer Science Peking University China Institute of Computing Technology Chinese Academy of Sciences China

The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects. Observing the generalization and reasoning capabilities of Multimodal Large Language Models (MLLMs), previous approaches have aimed to utilize these models to enhance robotic systems accordingly. However, these methods typically focus on high-level planning corrections using an additional MLLM, with limited utilization of failed samples to correct low-level contact poses which is particularly prone to occur during articulated object manipulation. To address this gap, we propose an Autonomous Interactive Correction (AIC) MLLM, which makes use of previous low-level interaction experiences to correct SE(3) pose predictions for articulated object. Specifically, AIC MLLM is initially fine-tuned to acquire both pose prediction and feedback prompt comprehension abilities. We design two types of prompt instructions for interactions with objects: 1) visual masks to highlight unmovable parts for position correction, and 2) textual descriptions to indicate potential directions for rotation correction. During inference, a Feedback Information Extraction module is introduced to recognize the failure cause, allowing AIC MLLM to adaptively correct the pose prediction using the corresponding prompts. To further enhance manipulation stability, we devise a Test Time Adaptation strategy that enables AIC MLLM to better adapt to the current scene configuration. Finally, extensive experiments are conducted in both simulated and real-world environments to evaluate the proposed method. The results demonstrate that our AIC MLLM can efficiently correct failure samples by leveraging interaction experience prompts. Our project website is https://***/view/aic-mllm. © 2024 Proceedings of Machine Learning Research.

关键词： robot programming

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Conception of a robotic Digital Shadow in Augmented Reality for Enhanced Human-robot Interaction 57

Conception of a Robotic Digital Shadow in Augmented Reality ...

引用

18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Adler, Fabian Gusenburger, Daniel Blum, Anne Müller, Rainer Department of Assembly Systems Center for Mechatronics and Automation Technology GGmbH Saarbrücken66121 Germany Department of Natural Sciences and Technology Saarland University Saarbrücken66123 Germany

A major factor for a successful interaction between a human and a robot is mutual trust and awareness for the situation. However, a robot is often perceived by humans as a moving and complex black box, which does not foster acceptance. Consequently, the research work published in this paper deals with the conceptional aspects and integration of a robotic digital shadow in Augmented Reality (AR). The objective is to foster acceptance between humans and robots, to raise awareness and to enhance the efficiency as well as effectiveness of collaborative efforts. Therefore, the challenges associated with the implementation of human-robot interaction for robot programming and live operations in AR are addressed. The paper discusses essential conceptional strategies, technological considerations and the resulting impact on the interaction between humans and robots, especially in a human-robot collaboration. In the theoretical part, an outline of the current status of the necessity and added value of such work is provided. The practical part conceptually describes the planned future implementation of the robotic digital shadow. ROS is used for robot programming and Unity for AR programming. A UR10e from Universal robots will be linked later as the robot system with a Microsoft HoloLens 2 as the AR system. A key aspect of setting up such a system is the architecture and the communication process as well as flow between the devices. This also includes the workspace calibration of the robot and the AR device. As a result, the necessary main pillars and further necessities of a possible implementation for a robotic digital shadow in AR were demonstrated. © 2024 The Authors.

关键词： robot programming

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BTR-RRT∗: Biased Towards Path Reference Points Path Planning 24

BTR-RRT∗: Biased Towards Path Reference Points Path Plannin...

引用

3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Gu, Julong Xu, Lingyue Liu, Wu Huang, Xing Li, Kuo China Communications First Highway Engineering Fifth Engineering Co. Ltd China Co. Ltd Suzhou China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University China

ISBN: (纸本)9798400716416

To enhance the efficiency of robot path planning and address the issue of blind search in sampling-based algorithms, this paper introduces a new sampling algorithm called biased towards path reference point RRT∗(BTR-RRT∗) based on neural networks and RRT∗. The algorithm operates as follows: initially, the trained neural network model is employed to gather map environment information, as well as the location details of starting and ending points. Then, the model predicts the probability distribution of feasible path nodes areas. Subsequently, during the sampling search process, the predicted nodes areas and state space are randomly sampled with different probabilities to obtain an initial feasible path. We compared the BTR-RRT∗algorithm with the traditional RRT∗algorithm, and the simulation results showed that the BTR-RRT∗algorithm significantly improved the efficiency of solving the initial solution. Furthermore, the quality of the initial solution is notably superior, facilitating the iterative optimization of the algorithm. © 2024 ACM.

关键词： robot programming

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Working space planning for wheeled robot overturning stability 11

Working space planning for wheeled robot overturning stabili...

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2024 11th International Conference on Advanced Manufacturing Technology and Materials Engineering, AMTME 2024

作者： Xing, Haoxin Li, Tiejun Liu, Jinyue Hebei University of Technology Tianjin300000 China Hebei University of Technology and Science Shijiazhuang050000 China

Mobile manipulators have been widely used in the field of building construction. Typical features of construction robots include crossing various rugged terrains in construction sites, carrying heavy payloads, and performing tasks in a large workspace, which may lead to unstable posture or overturning. The tipping of the mobile manipulator, especially when carrying a payload, will cause danger to the surrounding environment and staff, leading to the termination of the task. The purpose of this study is to establish a real-time tipping prediction system, which can be used to avoid tipping of mobile manipulators. By employing the CAD model of the robot and parameters such as the center of gravity and direction, we can ascertain and assess the instability of the robot and the overturning algorithm, thus providing early warning and averting any potential danger. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： robot programming

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Causal Action Network Heuristic-Based Action Space Search for Automated Planning 15

Causal Action Network Heuristic-Based Action Space Search fo...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cho, Joonmyun Oh, Hyun-Woo Electronics and Telecommunications Research Institute Digital Convergence Research Laboratory Daejeon Korea Republic of

ISBN: (纸本)9798350364637

There is a growing interest in automated planning as a promising technique for autonomous agents, such as robots, to achieve a given goal (mission) by devising their own action plans. Recently, search-based planning using state-of-the-art heuristics has made remarkable progress compared to the past, mostly achieved by using a combination of multiple heuristics. In this paper, we introduce a novel quantitative notion called the 'causal action network heuristics' and propose a way to exploit this notion within heuristic search, specifically through action space search tree pruning and action cost estimation. The paper discusses how to construct the causal action network and how to compute the causal involvement between the current node and the new nodes when expanding the front nodes of the action space search tree. Experiments show that the causal action network heuristics improve the performance of the GBFS algorithm. © 2024 IEEE.

关键词： robot programming

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The HA Based robot Navigation using MBS Method 4

The HA Based Robot Navigation using MBS Method

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Murugan, G. Al-Farouni, Mohammed Al-Hilali, Aqeel Vats, Satvik Bader, Nabaa M. Al-Ansari, Ahmed Taaban, Nabaa Kareem Gitam University 207 Nagadenehalli Doddaballapur taluk Karnataka Bengaluru561203 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Graphic Era Hill University Department of Cse India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India National Universitsy of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-kunooze University College Dental Department Basra Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Multiple methods of human-aware navigation are developing in tandem with the emergence of social robots. This article presents a comprehensive standard for the quantitative evaluation of robot navigation methods. System designers may use the Social robot Planner Benchmark (SRPB), an automated quantitative tool that generates consistent performance indicators for algorithms, to choose the optimum strategy for certain applications. As an alternative to conventional measures of work performance, our standard provides innovative social metrics such as the degree to which humans feel comfortable in the robot's environment and the degree to which robot actions seem natural, with a focus on how reliable human tracking is. Efficient navigation of service robots in scenarios involving human assistance requires a delicate balance between optimizing the robot's path and ensuring user comfort and safety. Our approach employs Bayesian inference for predicting the user's navigation goal and time-dependent path planning to achieve this balance effectively. By leveraging an interaction model and computing the belief about the user's intended destination, our method provides accurate predictions regularly updated based on new observations. Through a comprehensive evaluation, we demonstrate that our approach maintains a comfortable separation between the robot and the user while complying with social distance norms and minimizing the difference in arrival time. Statistical analysis reveals the superiority of our approach in terms of human-robot distance, significantly enhancing user comfort and safety during navigation scenarios. © 2024 IEEE.

关键词： robot programming

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Accelerating Search-Based Planning for Multi-robot Manipulation by Leveraging Online-Generated Experiences 34

Accelerating Search-Based Planning for Multi-Robot Manipulat...

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Shaoul, Yorai Mishani, Itamar Likhachev, Maxim Li, Jiaoyang Carnegie Mellon University United States

ISBN: (纸本)9781577358893

An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensional composite state space renders many well-known motion planning algorithms intractable. Recently, Multi-Agent Path-Finding (MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous guarantees. However, widely used conflict-based methods in MAPF assume an efficient single-agent motion planner. This poses challenges in adapting them to manipulation cases where this assumption does not hold, due to the high dimensionality of configuration spaces and the computational bottlenecks associated with collision checking. To this end, we propose an approach for accelerating conflict-based search algorithms by leveraging their repetitive and incremental nature - making them tractable for use in complex scenarios involving multi-arm coordination in obstacle-laden environments. We show that our method preserves completeness and bounded sub-optimality guarantees, and demonstrate its practical efficacy through a set of experiments with up to 10 robotic arms. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： robot programming

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Design of a Service robot System Based on a Multimodal Large Model 6

Design of a Service Robot System Based on a Multimodal Large...

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6th International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2024

作者： Ni, Mingze Xu, Gang Li, Hongsen Luo, Zhaoming Pang, Limin Yu, Binchao Ningbo University Ningbo Faculty of Mechanical Engineering and Mechanics Zhejiang 315201 China Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China Guilin University of Electronic Technology Guangxi Guilin541010 China Ningbo University Zhejiang Ningbo315201 China Ningbo Dalong Agricultural Technology Co. Ltd Zhejiang Ningbo315201 China PipeChina Zhejiang Pipeline Network Co. LTD. Zhejiang Ningbo315201 China

ISBN: (纸本)9798331528942

In recent years, significant progress has been made in the fields of vision, language, and robotics, driving the development of generalist robot and bringing them closer to people's daily lives. Facing complex unstructured environments, robots need to possess a series of key capabilities, including scene building, mobile navigation, task execution, scene understanding and task planning. In this paper, a service robot system based on a multimodal large model is proposed. The main elements of the system cover the hardware system design of the service robot, the development of scene building algorithms, the mobile navigation strategy based on the TEB (Timed Elastic Band) path planning algorithm, the robot grasping based on the pre-trained model, the fine task manipulation based on learning, and the integrated solution for task planning relying on the visual-linguistic model. To evaluate the system performance, several tasks were designed in a laboratory scenario and several experiments were conducted. The results show that the system performs well in scene building, mobile navigation and robot static scene manipulation, but the overall performance still needs to be further improved. Finally, prospects for future work include enhancing simulations and incorporating force sensors. © 2024 IEEE.

关键词： robot programming

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Enriching Process Models with Relevant Process Details for Flexible Human-robot Teaming 19th

Enriching Process Models with Relevant Process Details for...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Fichtner, Myriel Sucker, Sascha Riedelbauch, Dominik Jablonski, Stefan Henrich, Dominik University of Bayreuth Universitätsstrasse 30 Bayreuth95447 Germany

ISBN: (纸本)9783031545306

Human-robot teaming is crucial for future automation in small and medium enterprises. In that context, domain-specific process models are used as an intuitive description of work to share between two agents. Process designers usually introduce a certain degree of abstraction into the models. This way, models are better to trace for humans, and a single model can moreover enable flexibility by capturing several process variations. However, abstraction can lead to unintentional omission of information (e.g., experience of skilled workers). This may impair the quality of process results. To balance the trade-off between model readability and flexibility, we contribute a novel human-robot teaming approach with incremental learning of relevant process details (RPDs). RPDs are extracted from imagery during process execution and used to enrich an integrated process model which unifies human worker instruction and robot programming. Experiments based on two use cases demonstrate the practical feasibility and scalability of our approach. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

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