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2024 International Conference on Intelligent robots and Systems

作者： Ondika, Patrick Mrazek, Jan Barnat, Jiri Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9798350377712;9798350377705

Metamorphic robots have gained the attention of many researchers due to their ability to change shape and adapt to various tasks. In order to utilize the versatility of metamorphic systems, we need to be able to find a shape-shifting (reconfiguration) plan efficiently;however, finding these plans is challenging due to the high degree of freedom of modular systems. Reconfiguration algorithms proposed so far either scale poorly with a growing number of modules, impose specific restrictions on modules, or produce plans that are unrealistic outside of zero-gravity environments. This paper presents a new approach to the reconfiguration problem of chain-type metamorphic robots. Our algorithm relies on forming tentacles and using them to transport modules, which allows us to search through a reduced state space by computing many smaller planning instances. As a result, we obtain a heuristic solution that is more scalable than optimal planners, while producing realistic plans that impose no specific module requirements.

关键词： robot programming

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Statler: State-Maintaining Language Models for Embodied Reasoning

Statler: State-Maintaining Language Models for Embodied Reas...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yonede, Takuma Fan, Jiading Li, Peng Zhang, Huanyu Jiang, Tianchong Li, Shengjie Picker, Ben Yunis, David Mei, Hongyuan Walter, Matthew R. Toyota Technol Inst Chicago Chicago IL 60637 USA Fudan Univ Shanghai Peoples R China Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350384581;9798350384574

There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their abilities to reason about the histories of their actions and observations. In this paper, we explore a new dimension in which large language models may benefit robotics planning. In particular, we propose Statler, a framework in which large language models are prompted to maintain an estimate of the world state, which are often unobservable, and track its transition as new actions are taken. Our framework then conditions each action on the estimate of the current world state. Despite being conceptually simple, our Statler framework significantly outperforms strong competing methods (e.g., Code-as-Policies) on several robot planning tasks. Additionally, it has the potential advantage of scaling up to more challenging long-horizon planning tasks. We release our code here.

关键词： robot programming

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3D Global Path Planning for Walking robots on Sparse Volumetric Maps

3D Global Path Planning for Walking Robots on Sparse Volumet...

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2024 International Conference on Intelligent robots and Systems

作者： Besselmann, Marvin Grosse Haeuselmann, Ramona Mauch, Samuel Puck, Lennart Schnell, Tristan Roennau, Arne Dillmann, Ruediger FZI Res Ctr Informat Technol Dept Interact Diag & Serv Syst IDS Haid & Neu Str 10-14 D-76131 Karlsruhe Germany

ISBN: (纸本)9798350377712;9798350377705

The use of mobile robots has become increasingly common in multiple areas of daily life. To increase their autonomy for performing various tasks, efficient navigation skills are essential. The most crucial component of such navigation is the ability to calculate a global path between two points. The global path planning problem for mobile robots is typically limited to two-dimensional environments, in which the environment is projected onto a planar surface. While this approach works well in structured environments like industrial settings, it may not be suitable for all applications of mobile robots. With modern walking robots, capable of navigating complex terrain, more advanced path planning approaches are necessary. This work proposes a path-planning approach that utilizes the entire three-dimensional space, allowing for navigation in even the most challenging terrain. The central idea is to extend a traditional A* path planner to work directly on a fast volumetric map structure to generate optimal paths through the environment. Multiple optimizations and adjustments are introduced to improve the algorithm's performance. By applying morphology operators to sparse maps, sensor inaccuracies during the map construction are mitigated. Additionally, adjustments are made to handle the added complexity introduced by the extra search space dimension and to comply with the limitations of autonomous walking robots. This is paired with an efficient caching strategy to enhance the overall path-planning speed. The capability of the path planning approach is evaluated using both artificial and real-world maps. The results demonstrate that this approach shows great potential for enabling mobile ground robots to autonomously navigate even the most demanding terrains utilizing the entire three-dimensional space.

关键词： robot programming

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Enabling Maintainablity of robot Programs in Assembly by Extracting Compositions of Force- and Position-Based robot Skills from Learning-from-Demonstration Models

Enabling Maintainablity of Robot Programs in Assembly by Ext...

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2024 International Conference on Intelligent robots and Systems

作者： Bargmann, Daniel Kraus, Werner Huber, Marco F. Fraunhofer Inst Mfg Engn & Automat Stuttgart Germany Univ Stuttgart Inst Ind Mfg & Management IFF Stuttgart Germany

ISBN: (纸本)9798350377712;9798350377705

To this day, only a small number of industrial robots is used in assembly. One key reason for this is that specific contact situations require the introduction of force-control schemes. The parameters for those schemes are hard to select in practice, because they require in-depth expertise about the robot and the process. Learning-from-Demonstration (LfD) provides a powerful approach to intuitively parameterize robot programs by demonstrating the task at hand. However, when dimensions increase by including force or orientation, many LfD algorithms are hard to verify, understand and maintain, requiring expert knowledge to make adaptions, effectively making it a "black-box". This property renders them ineffective for usage in industrial applications. We build upon a system of composable skills, that can be easily adapted by experts without the need to demonstrate the task again. This approach to skill-based robot programming promises to address the issues of readability and maintainability by sequencing robot movements in skills and breaking them down into understandable (sub)goals. In this paper, we combine skill-based programming with LfD, preserving both maintainability and intuitive parameterization. We present (a) an approach to parameterize and create sequences of hierarchies of force- and/or position-controlled robot skills from a LfD model, (b) which can be adapted by a user by hand with few, basic and understandable parameters, and (c) show its applicability on the real-world example of terminal clamp assembly. We achieve a reduction in teach-in time of 53.8% for variants, increased robustness against variance, and efficient tight stacking of clamps with a gap of <= 1mm.

关键词： robot programming

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Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture 1

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7th International Russian Automation Conference, RusAutoCon 2024

作者： Mikhailov, V.S. Khairullin, I.D. Svyatov, K.V. Ulyanovsk State Technical University 32 Severny Venets Str Ulyanovsk432027 Russia

ISBN: (数字)9783031824944

ISBN: (纸本)9783031824937

In Russia, agriculture is one of the key industries, so the development of innovative solutions in this area is of great importance. A promising approach is to automate field operations using highly automated manufacturing. These vehicles provide many benefits, including increased productivity, accurate information delivery, resource optimization, improved worker safety, flexibility, scalability, addressing labor shortages and relative technological innovation. The purpose of this work is to develop software for controlling unmanned vehicles and effectively managing the field processing devices installed on them. Testing algorithms on your own cars is ineffective due to high resource consumption and reduced comfort. Therefore, a highly automated vehicle simulator was used to debug the algorithm. The simulator offers various features such as route generation using Reeds-Scheppe curves, multiple path identification algorithms, visualization of landscape impacts, metric graphing capabilities, and robust compatibility with real vehicles. This paper proposes the development and implementation of these tools, considering their capabilities and benefits for promoting automation in agriculture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： robot programming

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Autonomous robotic Exploration Based on Rolling Bias-RRT Algorithm 24

Autonomous Robotic Exploration Based on Rolling Bias-RRT Alg...

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4th International Joint Conference on robotics and Artificial Intelligence, JCRAI 2024

作者： Zhu, Kai Guan, Yingzi Heilongjiang University of Science and Technology Heilongjiang Harbin China Harbin Institute of Technology Harbin China

ISBN: (纸本)9798400710100

In order to improve the efficiency and autonomy of unknown region exploration for robots, an autonomous exploration algorithm based on rolling window method and bias-RRT algorithm is proposed. The rolling window method is adopted for global exploration planning, and the bias-RRT algorithm is adopted for local path planning. To improve the exploration efficiency and path quality, the evaluation function for candidate local goals is optimized considering information gain, the distance to obstacle and turning angle. Based on artificial potential field algorithm, the growth of an RRT is guided to avoid obstacles autonomously and move forward to the sub-goal rapidly. Considering exploration efficiency and autonomous obstacle avoidance simultaneously, the extend length of RRT is adaptively tuned. The global goals are updated and backtracked to avoid getting stuck in local areas, such that the completeness of the exploration is improved. Simulation results show that compared to the classical method, the proposed autonomous exploration method can improve the exploration completeness in a convoluted environment. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： robot programming

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Optimized Impedance Control for Biped robots in IUA Using Reinforcement Learning and Quadratic programming

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IEEE Internet of Things Journal 2025年

作者： Hou, Yunfeng Tang, Jun Ma, Zhiyuan Li, Jialong Li, Wei University of Shanghai for Science and Technology Institute of Machine Intelligence Shanghai200093 China University of Shanghai for Science and Technology School of Intelligent Emergency Management Shanghai200093 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Lixin University of Accounting and Finance School of Finance Shanghai201209 China

In the field of the Internet of Unmanned Agents (IUA), autonomous devices often struggle to maintain stability, adaptability, and coordination in dynamic environments. Current control strategies are hindered by difficulties such as real-time adaptation to environmental changes, handling sensor noise, and managing the coordination of multiple agents under complex, variable task constraints. These challenges limit the practical deployment of robots in IUA systems, making it crucial to develop more robust and efficient control methods. To address these issues, we propose a reinforcement learning-based impedance coefficient optimization approach to improve robots’ stability and responsiveness. By integrating impedance control, the Divergent Component of Motion (DCM) model, reinforcement learning (RL), and quadratic programming (QP), this approach dynamically adjusts the robot’s stiffness, damping, and inertia coefficients in real-time. Specifically, the Soft Actor-Critic (SAC) algorithm is used for optimization, while QP ensures that control forces remain feasible across multiple tasks and constraints. This strategy enhances the robots’ adaptability and coordination in complex IUA. Experimental results validate the approach, significantly improving robot performance in IUA systems. © 2014 IEEE.

关键词： robot programming

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Collision Free Path Planning for Underwater Vehicles in Rapidly Changing Environments

Collision Free Path Planning for Underwater Vehicles in Rapi...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Pesson, Mason Morgan, Edward Barbalata, Corina Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

ISBN: (纸本)9798350355376;9798350355369

This paper presents an obstacle avoidance path planning algorithm designed to generate smooth paths for underwater robotic systems that operate in dynamic environments. Using the kinematics of the system, an initial path is generated which is further optimized considering the constraints of the system and the environment. The correlation between path states is embedded into a kernel used throughout the optimization. This produces a more informative optimization process that leads to changes in one state based on all other states. However, the use of this correlation between path states may lead to an exhaustive computational effort for highly dimensional systems. Therefore, the proposed approach, named AmaxGPMP, introduces a strategy capable of reducing the needed information to develop these kernels while accurately describing the correlation among states, hence decreasing the computation time. The proposed path planner was tested in simulation and experimentally on a BlueROV2 Heavy vehicle that was modified to enable autonomous capabilities. The results demonstrate the ability of AmaxGPMP to successfully generate smooth, feasible, and safe behaviors for autonomous underwater vehicles.

关键词： robot programming

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Multi-Agent Path Finding with Real robot Dynamics and Interdependent Tasks for Automated Warehouses 27

Multi-Agent Path Finding with Real Robot Dynamics and Interd...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lehoux-Lebacque, Vassilissa Silander, Tomi Loiodice, Christelle Lee, Seungjoon Wang, Albert Michel, Sofia NAVER LABS Europe France NAVER LABS HQ Korea Republic of

ISBN: (纸本)9781643685489

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and *** the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life *** this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to *** creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these *** solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving *** prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse. © 2024 The Authors.

关键词： robot programming

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A Study on Mobile robot Path Planning in Constrained Environments Using an Enhanced RRT Algorithm 4th

A Study on Mobile Robot Path Planning in Constrained Environ...

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4th International Conference on Advanced Intelligent Technologies, ICAIT 2023

作者： Wang, Wenteng Dalian University of Technology Liaoning China

ISBN: (纸本)9789819732098

This research aims to solve the path planning problem for mobile robots in narrow environment roads by means of an improved Rapid Exploration Random Tree (RRT) algorithm. Narrow environment roads usually have restricted space, making traditional path planning methods challenging. This study combines the advantages of the RRT algorithm and introduces some improvements to accommodate this complex situation. First, we modify the extension strategy of RRT and introduce an intelligent local path search method to better explore feasible paths in narrow environments. This localized path search strategy helps the robot to overcome obstacles in narrow regions while maintaining path validity. Second, we employ a dynamic sampling strategy to increase the search efficiency of RRT. As the robot approaches the target region, the sampling density increases to converge to the optimal path faster. Finally, we verify the performance of the proposed algorithm through simulation and real robot experiments. The experimental results show that the improved RRT algorithm is able to plan paths more efficiently in narrow environment roads, enabling the mobile robot to successfully traverse complex passages and crowded spaces. This study provides an effective solution for mobile robot path planning in narrow environments, which can help robots better cope with challenges such as indoor navigation and rescue missions in practical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： robot programming

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