Robotics and Autonomous Systems最新文献_第3页

Online learning for agile underwater maneuvering: Gaussian processes and sparse regression for data-driven model predictive control 敏捷水下机动的在线学习：数据驱动模型预测控制的高斯过程和稀疏回归

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-10-03 DOI: 10.1016/j.robot.2025.105211

Sriharsha Bhat , Giancarlo Troni , Ivan Stenius

{"title":"Online learning for agile underwater maneuvering: Gaussian processes and sparse regression for data-driven model predictive control","authors":"Sriharsha Bhat , Giancarlo Troni , Ivan Stenius","doi":"10.1016/j.robot.2025.105211","DOIUrl":"10.1016/j.robot.2025.105211","url":null,"abstract":"<div><div>Autonomous underwater vehicles (AUVs) show much promise in environmental sensing, aquaculture, and security applications. Robust and adaptive control strategies can immensely benefit these scenarios by increasing autonomy and endurance. However, AUVs are nonlinear systems whose dynamics are challenging to model, especially during agile maneuvers at high angles of attack. To better capture these nonlinear effects, this paper proposes a physics-informed system identification scheme that combines prior knowledge of the system dynamics with data-driven regression. Strategies including Sparse Identification of Nonlinear Dynamics (SINDy), nonlinear least squares regression, and Gaussian processes (GPs) are used to learn the AUV dynamics online from measured data. These data-driven models are then implemented in an adaptive model predictive controller (MPC) for agile maneuvering that drives the system to a set point while updating the prediction model when new measurements are available. The performance of these three system identification strategies is evaluated on two different 6-DOF AUV platforms. All three strategies show good real-time performance, while the GP model offers the best balance between accuracy, speed and robustness. Field experimental data from the SAM AUV and the MOLA AUV are used for performance evaluation.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105211"},"PeriodicalIF":5.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust walking motion generation for biped robots using manipulability-based reinforcement learning 基于可操作性强化学习的双足机器人鲁棒行走运动生成

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-10-01 DOI: 10.1016/j.robot.2025.105209

Amin Tadayyoni , Behnam Miripour Fard , Ali Jamali

{"title":"Robust walking motion generation for biped robots using manipulability-based reinforcement learning","authors":"Amin Tadayyoni , Behnam Miripour Fard , Ali Jamali","doi":"10.1016/j.robot.2025.105209","DOIUrl":"10.1016/j.robot.2025.105209","url":null,"abstract":"<div><div>In reinforcement learning, designing an effective reward function is essential for developing and controlling humanoid robots. The criteria for replicating human learning and achieving human-like responses in bipedal robots remain unclear. Integrating kinematic and dynamic characteristics into the reward function, along with the use of detailed models, can enhance efficiency and robustness. This study proposes a novel manipulability-based reward function within an end-to-end learning framework, enabling the agent to autonomously generate robust, real-time movements. Incorporating the kinematic manipulability index into the proposed reward function significantly improves the robot's locomotion behavior and ability to handle disturbances. Results indicate that incorporating kinematic manipulability into training enhances the robot's forward speed and improves its ability to handle sagittal and lateral disturbances, as well as uncertainties in length and weight distribution. Furthermore, compared to a classical hierarchical controller, the trained agent attained higher speeds and demonstrated superior disturbance handling, validating the effectiveness of the proposed learning-based approach. These findings highlight the significance of incorporating kinematic manipulability into the reward function to enhance the agility and adaptability of bipedal robots.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105209"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rolling mechanism and performance of a soft robot driven by local curvature loading 局部曲率加载驱动软机器人滚动机理及性能研究

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-10-01 DOI: 10.1016/j.robot.2025.105212

Pengfei Yang , Luyu Gao , Ruixing Huang , Yuyang Xiong , Yuqing Mao , Feng Huang , Fei Dang

{"title":"Rolling mechanism and performance of a soft robot driven by local curvature loading","authors":"Pengfei Yang , Luyu Gao , Ruixing Huang , Yuyang Xiong , Yuqing Mao , Feng Huang , Fei Dang","doi":"10.1016/j.robot.2025.105212","DOIUrl":"10.1016/j.robot.2025.105212","url":null,"abstract":"<div><div>Previous rolling soft robots are usually hard to achieve balanced rolling performance (terrain adaptability, rolling velocity and energy efficiency). This paper proposes a rolling soft robot driven by local curvature loading, which demonstrates good rolling velocity, small deformation rate, good energy efficiency and excellent terrain adaptability. A theory based on the energy method is established to analyze the rolling mechanism of the soft robot and to determine the critical loading curvature, which is validated by experiments. The local curvature loading causes the deformation of the entire robot configuration and results in the shift of the gravity center, which generates a gravity torque to drive the rolling of the soft robot when the critical loading curvature is reached. The proposed soft robot has good average rolling velocity (182.9 mm/s or 0.938 body length per second, BL/s) and can adapt to a variety of complex terrains such as the stairs (stair height 15 mm), the slope (slope angle 12.4 °) and the wide broken bridge (gap length 100 mm or 0.526 BL). The study in this work demonstrates broad application prospect in the fields of biomedical therapy, exploration, searching and rescuing, which provides a new idea for the structural design and performance improvement of rolling soft robots.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105212"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asymmetric bounded fuzzy adaptive control for uncertain coordinative multiple robot manipulators 不确定多机器人协调臂的非对称有界模糊自适应控制

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-26 DOI: 10.1016/j.robot.2025.105198

Yongqing Fan, Lin Yang, Zhen Li

{"title":"Asymmetric bounded fuzzy adaptive control for uncertain coordinative multiple robot manipulators","authors":"Yongqing Fan, Lin Yang, Zhen Li","doi":"10.1016/j.robot.2025.105198","DOIUrl":"10.1016/j.robot.2025.105198","url":null,"abstract":"<div><div>This work presents a fuzzy adaptive control technology for determining the desired trajectory of collaborative robot manipulators when grasping a general object. While the classical fuzzy logic systems (FLSs) are commonly used to compensate for some unknown nonlinear continuous functions, their approximation accuracies are often limited. To address this issue, a non-zero time-varying parameter is introduced in the input of Mamdani type or Takagi–Sugeno (T–S) type FLSs. This parameter allows for universal approximation, enabling the system to automatically adjust the approximation precision through adaptive rules. The unknown nonlinear continuous functions are represented using a combined form of homogeneous functions, which are then approximated using FLSs. Unlike previous fuzzy adaptive control schemes, this approach overcomes the limitation of a finite universal approximation domain. Additionally, the proposed method can calculate the coefficients of consequents in T–S type FLSs, reducing the computational load of the controller. The effectiveness of the proposed sliding mode surface is demonstrated in ensuring the required tracking performance, with all signals in the closed-loop system being uniformly ultimately bounded (UUB). The efficiency of the control scheme is further demonstrated through various simulation results.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105198"},"PeriodicalIF":5.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Reinforcement Learning for Zero-Shot Coverage Path Planning with Dynamic UAV Networks 基于多智能体强化学习的动态无人机网络零射击覆盖路径规划

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-26 DOI: 10.1016/j.robot.2025.105163

José P. Carvalho, A. Pedro Aguiar

{"title":"Multi-Agent Reinforcement Learning for Zero-Shot Coverage Path Planning with Dynamic UAV Networks","authors":"José P. Carvalho, A. Pedro Aguiar","doi":"10.1016/j.robot.2025.105163","DOIUrl":"10.1016/j.robot.2025.105163","url":null,"abstract":"<div><div>Recent advancements in autonomous systems have enabled the development of intelligent multi-robot systems for dynamic environments. Unmanned Aerial Vehicles play an important role in multi-robot applications such as precision agriculture, search-and-rescue, and wildfire monitoring, all of which rely on solving the coverage path planning problem. While Multi-Agent Coverage Path Planning approaches have shown potential, many existing methods lack the scalability and adaptability needed for diverse and dynamic scenarios. This paper presents a decentralized Multi-Agent Coverage Path Planning framework based on Multi-Agent Reinforcement Learning with parameter sharing and Centralized Training with Decentralized Execution. The framework incorporates a customized Rainbow Deep-Q Network, a size-invariant reward function, and a robustness and safety filter to ensure completeness and reliability in dynamic environments. Our training pipeline combines curriculum learning, domain randomization, and transfer learning, enabling the model to generalize to unseen scenarios. We demonstrate zero-shot generalization on scenarios with significantly larger maps, an increased number of obstacles, and a varying number of agents compared to what is seen during training. Furthermore, the models can also adapt to more structured maps and handle different tasks, such as search-and-rescue, without the need for retraining.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105163"},"PeriodicalIF":5.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrative AI framework for robotics: LLM-enabled reinforcement learning in object manipulation and task planning 机器人集成人工智能框架：llm在对象操作和任务规划中的强化学习

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-25 DOI: 10.1016/j.robot.2025.105197

Truong Nhut Huynh, Kim-Doang Nguyen

{"title":"Integrative AI framework for robotics: LLM-enabled reinforcement learning in object manipulation and task planning","authors":"Truong Nhut Huynh, Kim-Doang Nguyen","doi":"10.1016/j.robot.2025.105197","DOIUrl":"10.1016/j.robot.2025.105197","url":null,"abstract":"<div><div>The paper develops an innovative hybrid AI framework that combines contextual reasoning of a large language model (LLM) with adaptivity of reinforcement learning (RL) for improved robotic object manipulation and task execution. In particular, the proposed system integrates high-level task planning, where GPT-4 and an RL submodule collaboratively generate optimized task strategies, with low-level real-time control through RL, allowing for enhanced adaptability in dynamic environments. The experimental results demonstrate significant improvements in task success rates and operational efficiency compared to standalone RL and GPT-4 approaches. In static environments, the integrative approach achieved a 90% task success rate, with an average completion time of 42.1 s and only 1.1 retries, outperforming RL-only (72%) and GPT-4-only (78%) methods. In dynamic environments, our integrative system maintained an 85% success rate, compared to 65% for RL-only and 70% for GPT-4-only. For complex tasks, the hybrid model showed a substantial advantage, with an 80% success rate, highlighting its superior performance in tasks requiring both high-level reasoning and low-level precision control.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105197"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DWA-3D: A reactive planner for robust and efficient autonomous UAV navigation in confined environments DWA-3D：用于受限环境下鲁棒高效自主无人机导航的响应式规划器

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-25 DOI: 10.1016/j.robot.2025.105196

Jorge Bes, Juan Dendarieta, Luis Riazuelo, Luis Montano

{"title":"DWA-3D: A reactive planner for robust and efficient autonomous UAV navigation in confined environments","authors":"Jorge Bes, Juan Dendarieta, Luis Riazuelo, Luis Montano","doi":"10.1016/j.robot.2025.105196","DOIUrl":"10.1016/j.robot.2025.105196","url":null,"abstract":"<div><div>Despite the growing impact of Unmanned Aerial Vehicles (UAVs) across various industries, most of the current available solutions lack a robust autonomous navigation system to deal with the appearance of obstacles safely. This work presents an approach to perform autonomous UAV planning and navigation in indoor or confined scenarios where a safe and high maneuverability is required, due to the cluttered environment and narrow rooms. The system combines an RRT* global planner with a newly proposed reactive planner, DWA-3D, which is an extension of the well-known <em>DWA</em> method for 2D robots. We provide a theoretical-empirical method for adjusting the parameters of the objective function to optimize, which eases the classical difficulty for tuning them. An onboard LiDAR provides a 3D point cloud, which is projected on an OctoMap in which the planning and navigation decisions are made. There is not a prior map; the system builds and updates the map online, from the current and the past LiDAR information included in the OctoMap. Extensive real-world experiments were conducted to validate the system and to obtain a fine-tuning of the involved parameters. These experiments allowed us to provide a set of values that ensure safe operation across all the tested scenarios. Just by weighting two parameters, it is possible to prioritize either horizontal path alignment or vertical (height) tracking, resulting in enhancing vertical or lateral avoidance, respectively. Additionally, our DWA-3D proposal is able to navigate successfully even in absence of a global planner or with one that does not consider the drone’s size. Finally, the conducted experiments show that computation time with the proposed parameters is not only bounded but also remains stable at around 40 ms, regardless of the scenario complexity.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105196"},"PeriodicalIF":5.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A path planning decision system for unknown pipeline detection using UAVs 基于无人机的未知管道检测路径规划决策系统

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-24 DOI: 10.1016/j.robot.2025.105194

Dekai Lin, Ruitao Ma, Yin Zhao, Jiakuo Zhang, Shubin Liu, Hang Zhu

{"title":"A path planning decision system for unknown pipeline detection using UAVs","authors":"Dekai Lin, Ruitao Ma, Yin Zhao, Jiakuo Zhang, Shubin Liu, Hang Zhu","doi":"10.1016/j.robot.2025.105194","DOIUrl":"10.1016/j.robot.2025.105194","url":null,"abstract":"<div><div>This paper introduces the Pipeline-Aimed path Planner (PAPlanner), a sampling-based path planner for UAVs in unknown oil/gas pipelines. Its key contributions include a dynamic anchor point update strategy and path optimization that adapts to bends and diameter changes, eliminating redundant backtracking and enabling continuous exploration. By integrating real-time voxel maps, the algorithm optimizes paths to stay near the pipeline axis. Simulation results show that PAPlanner reduces average path length by 26.4% compared to the advanced MBPlanner method in the elbow scene experiment, demonstrating efficient safe trajectory maintenance. In the variable diameter scene experiment, where MBPlanner fails frequently, PAPlanner achieves a 0% failure rate. Real flight experiments validate its robustness with a 0.309 m average trajectory deviation from the axis, confirming reliable navigation. This work presents a novel framework enhancing UAV exploration efficiency in pipelines, overcoming limitations of existing algorithms for autonomous inspection in sensor-degraded confined environments.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105194"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CPP-DIP: Multi-objective coverage path planning for MAVs in dispersed and irregular plantations 分散和不规则人工林中MAVs的多目标覆盖路径规划

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-24 DOI: 10.1016/j.robot.2025.105193

Weijie Kuang, Hann Woei Ho, Ye Zhou

{"title":"CPP-DIP: Multi-objective coverage path planning for MAVs in dispersed and irregular plantations","authors":"Weijie Kuang, Hann Woei Ho, Ye Zhou","doi":"10.1016/j.robot.2025.105193","DOIUrl":"10.1016/j.robot.2025.105193","url":null,"abstract":"<div><div>Coverage Path Planning (CPP) is vital in precision agriculture to improve efficiency and resource utilization. In irregular and dispersed plantations, traditional grid-based CPP often causes redundant coverage over non-vegetated areas, leading to waste and pollution. To overcome these limitations, we propose CPP-DIP, a multi-objective CPP framework designed for Micro Air Vehicles (MAVs). The framework transforms the CPP task into a Traveling Salesman Problem (TSP) and optimizes flight paths by minimizing travel distance, turning angles, and intersection counts. Unlike conventional approaches, our method does not rely on GPS-based environmental modeling. Instead, it uses aerial imagery and a Histogram of Oriented Gradients (HOG)-based approach to detect trees and extract image coordinates. A density-aware waypoint strategy is applied: Kernel Density Estimation (KDE) is used to reduce redundant waypoints in dense regions, while a greedy algorithm ensures complete coverage in sparse areas. To verify the generality and scalability of the framework, TSP instances of varying sizes are solved using three methods: Greedy Heuristic Insertion (GHI), Ant Colony Optimization (ACO), and Monte Carlo Reinforcement Learning (MCRL). An object-based optimization is subsequently applied to further refine the paths. Additionally, CPP-DIP integrates ForaNav, our insect-inspired navigation method, for accurate tree localization and tracking. Experimental results show that MCRL provides a balanced solution, reducing travel distance by 16.9 % compared to ACO while maintaining comparable performance to GHI. It also improves path smoothness by reducing turning angles by 28.3 % and 59.9 % relative to ACO and GHI, respectively, and eliminates intersections. Computational resource comparisons further highlight that GHI scales efficiently with increasing waypoints, whereas ACO and MCRL incur higher computational costs. These results confirm the robustness, efficiency, and scalability of the proposed CPP-DIP.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105193"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Behavior-based navigation of a two-wheeled self-balancing robot using a modified hybrid automaton 基于行为的改进混合自动机两轮自平衡机器人导航

IF 5.2 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-09-19 DOI: 10.1016/j.robot.2025.105195

Mohsen Heydari Khalili, Majid Sadedel

{"title":"Behavior-based navigation of a two-wheeled self-balancing robot using a modified hybrid automaton","authors":"Mohsen Heydari Khalili, Majid Sadedel","doi":"10.1016/j.robot.2025.105195","DOIUrl":"10.1016/j.robot.2025.105195","url":null,"abstract":"<div><div>Due to advances in robotics science, mobile robots are being used in more and more applications worldwide, and the autonomous navigation of these robots is an important topic in their discussion. This paper focuses on the autonomous navigation of a two-wheeled self-balancing robot (TWSBR) in an unknown environment using behavior-based control in the form of a hybrid automaton. This hybrid automaton includes the behaviors “Go To Goal” and “Avoid Obstacle,” and to avoid the Zeno phenomenon between these two behaviors, another behavior is considered in between, called “Follow Wall,” which the robot uses to move around the obstacle. However, two bugs are identified in the conventional hybrid automaton. The first bug causes the robot to not follow the optimal path. Another bug is that the Zeno phenomenon occurs between the two behaviors “Follow Wall” and “Go To Goal,” causing odometry errors in the experimental environment. The results show that the modified hybrid automaton successfully corrects the bugs and works as intended. The navigation algorithm is designed for the point mass model, so it is transformed to the unicycle model using a transformation, which can be used as input to the TWSBR controller. After linearizing the dynamic equations of the robot around its equilibrium point, the pole placement method is used to create the TWSBR controller. By adding the Luenberger observer to estimate the state variables, the non-full-state feedback system is also controlled. The results of the simulations demonstrate that the whole system is functioning properly so that the robot follows the path determined by the navigation algorithm while maintaining its equilibrium.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"195 ","pages":"Article 105195"},"PeriodicalIF":5.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0