2026-06-29 · Features
Muqing Cao, Thien-Minh Nguyen, Shenghai Yuan, Andreas Anastasiou, Angelos Zacharia, Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou, Xinhang Xu, Mingjie Zhang, Fei Gao, Boyu Zhou, Ben M. Chen, Lithua Xie
Abstract: We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-uncrewed-aerial-vehicle (UAV) systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at the 2023 IEEE Conference on Decision and Control (CDC) and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This article examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task’s complexity and suggest promising research directions in cooperative multi-UAV systems. The simulation framework, including the source code and detailed instructions, is publicly available at https://ntu-aris.github.io/caric. For more about this article see link below. https://ieeexplore.ieee.org/document/11079769/a> For the open access PDF link of this article please click here . The post Cooperative Aerial Robot Inspection Challenge: A Benchmark for Heterogeneous Multi-Uncrewed-Aerial-Vehicle Planning and Lessons Learned appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Iuri Barros, Yoshito Okada, Kenjiro Tadakuma, Masahiro Watanabe, Masashi Konyo, Kazunori Ohno, Yoshiki Yokota, Ranulfo Bezerra, Satoshi Tadokoro
Abstract: Drone docking stations promote efficient operations of drones, but they usually support only one vehicle and are accessible primarily through vertical landing. These limitations hinder multidrone operations and result in challenges for fast precise docking, particularly under severe wind conditions. This article assesses the EAGLES Port, which uses a horizontal landing approach to address these challenges, and makes a performance comparison between horizontal and vertical landing through analysis of wind tunnel data with manually controlled drones. Results show that horizontal landing decreases the average landing duration by 35.58% and can achieve 59.67% faster docking compared to vertical landing in optimal conditions. The system also provides near-zero position error at docking and supports multiple drones. These advantages stem from improved flight stability, quicker alignment with landing targets, and a 2.8× higher average velocity compared to vertical landing. These results indicate that vertical landing is better suited for missions with wider landing zones and where delays in landing have mild consequences, whereas horizontal landing excels in scenarios where rapid accurate landings are critical. For more about this article see link below. https://ieeexplore.ieee.org/document/11074730/a> For the open access PDF link of this article please click here . The post Drone Landing Performance in Windy Conditions: Comparing the Vertical and Horizontal Landing Approaches With the EAGLES Port appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Simone Tolomei, Giovanni Di Lorenzo, Franco Angelini, Leopoldo de Simone, Emanuele Fanfarillo, Tiberio Fiaschi, Silvia Cannucci, Simona Maccherini, Paolo Remagnino, Claudia Angiolini, Manolo Garabini
Abstract: This paper presents a novel approach to forest habitat monitoring using robotics and advanced data analysis techniques. We introduce a quadrupedal robot with LiDAR and onboard cameras to collect detailed data about forest structure and composition. The data is then processed using a combination of data analysis techniques and machine learning algorithms to perform a comprehensive dendrometric and floristic survey. Our approach provides an efficient and accurate method for assessing the ecological health of forest ecosystems. This work contributes to the ongoing efforts in habitat conservation and offers a promising tool for future environmental monitoring tasks. For more about this article see link below. https://ieeexplore.ieee.org/document/11095737/a> For the open access PDF link of this article please click here . The post Harnessing Robotics for European Union Forest Habitats Monitoring: Toward a Robotic-Assisted Framework for Standardized Field Surveys appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Jaewon Byun, Joonsub Byun, Junsu Kang, Inje Yi, Jungwoo Lee, Kyoungseok Noh, Jongchan Kim, Youngho Choi, Goobong Chung, Sangrok Oh, Keehoon Kim
Abstract: This article explores the advent of an innovative autonomous robot designed to enhance hospital disinfection through targeted wiping and ultraviolet-C (UV-C) irradiation methods. The urgency for such advancements has been underscored by the COVID-19 pandemic, which revealed challenges, such as low compliance rates, physical fatigue, labor shortages, and heightened risk of pathogen exposure for disinfection workers. Traditional disinfection approaches, including UV-C irradiation mobile robots and hydrogen peroxide vapor methods, while effective, fall short in addressing obstacles like shaded areas and surface contaminants. To bridge this gap, we introduce a novel robot that combines physical wiping to remove contaminants with targeted UV-C irradiation for areas less amenable to wiping. Our development efforts have centered on optimizing disinfection efficacy and ensuring the robot’s reliability for practical applications in real-world hospitals For more about this article see link below. https://ieeexplore.ieee.org/document/10938586/a> For the open access PDF link of this article please click here . The post Autonomous Ultraviolet-C Disinfection and Wiping Robot: Assessment in a Hospital Environment appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Yuzhi Lai, Shenghai Yuan, Youssef Nassar, Mingyu Fan, Atmaraaj Gopal, Arihiro Yorita, Naoyuki Kubota, Matthias Rätsch
Abstract: Translating human intent into robot commands is crucial for the future of service robots in an aging society. Existing human–robot interaction (HRI) systems relying on gestures or verbal commands are impractical for the elderly, due to difficulties with complex syntax or sign language. To address the challenge, this article introduces a multimodal interaction framework that combines voice and deictic posture information to create a more natural HRI system. Visual cues are first processed by the object detection model to gain a global understanding of the environment, and then bounding boxes are estimated based on depth information. By using a large language model (LLM) with voice-to-text commands and temporally aligned selected bounding boxes, robot action sequences can be generated, while key control syntax constraints are applied to avoid potential LLM hallucination issues. The system is evaluated on real-world tasks with varying levels of complexity, using a Universal Robots UR3e manipulator. Our method demonstrates significantly better HRI performance in terms of accuracy and robustness. To benefit the research community and the general public, we made our code and design open source. For more about this article see link below. https://ieeexplore.ieee.org/document/10910098/a> For the open access PDF link of this article please click here . The post Natural Multimodal Fusion-Based Human–Robot Interaction: Application With Voice and Deictic Posture via Large Language Model appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Junyang Wang, XueAi Li, Fenglei Ni, Baoshi Cao, Le Qi, Hong Liu, Xiangji Wang, Teng Zhang
Abstract: The mobility and manipulation of bipedal humanoid robots always depend on their legs, which account for balance and may substantially accelerate energy consumption, especially when the lower body is expected to be stationary. To this end, this article presents a biomimetic and energy-efficient design for bipedal robots’ legs and extensively demonstrates its performance on the developed prototype. From a biomimetic perspective, human walking data are captured at first, and the range of motion, speed, and coupling relationship of various joints are analyzed. The skeletal and muscular structures of each joint are then dissected and imitated by mechanism synthesis, where a novel locking mechanism inspired by the biological structure of the knee joint is integrated. To better evaluate the energy consumption capability of legged robots, we propose a new metric—dynamic power of the system (DPoS)—and experimentally prove its rationality. The effectiveness and superiority of our design are ultimately validated through the comparative experiments on both our prototype and the off-the-shelf counterpart. For more about this article see link below. https://ieeexplore.ieee.org/document/10977650/a> For the open access PDF link of this article please click here . The post A Biomimetic and Energy-Efficient Leg Design for a Humanoid Robot: An Exclusively Linear-Actuated Implementation appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Gaoyuan Liu, Bas Boom, Naftali Slob, Yuri Durodié, Ann Nowé, Bram Vanderborght
Abstract: Pruning is an essential agricultural practice for orchards. Proper pruning can promote healthier growth and optimize fruit production throughout the orchard’s lifespan. Robot manipulators have been developed as an automated solution for this repetitive task, which typically requires seasonal labor with specialized skills. While previous research has primarily focused on the challenges of perception, the complexities of manipulation are often overlooked. These challenges involve planning and control in both joint and Cartesian spaces to guide the end effector through intricate, obstructive branches. Our work addresses the behavior planning challenge for a robotic pruning system, which entails a multilevel planning problem in environments with complex collisions. In this article, we formulate the planning problem for a high-dimensional robotic arm in a pruning scenario, investigate the system’s intrinsic redundancies, and propose a comprehensive pruning workflow that integrates perception, modeling, and holistic planning. In our experiments, we demonstrate that more comprehensive planning methods can significantly enhance the performance of the robotic manipulator. Finally, we implement the proposed workflow on a real-world robot. As a result, this work complements previous efforts on robotic pruning and motivates future research and development in planning for pruning applications. For more about this article see link below. https://ieeexplore.ieee.org/document/10978028/a> For the open access PDF link of this article please click here . The post Automated Behavior Planning for Fruit Tree Pruning via Redundant Robot Manipulators: Addressing the Behavior Planning Challenge appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Abdurrahman Yilmaz, Umut Dumandag, Aydin Cagatay Sari, Ismail Hakki Savci, Hakan Temeltas
Abstract: Autonomous mobile robots (AMRs) are revolutionizing industries by enhancing flexibility and efficiency, particularly in dynamic environments, such as automotive manufacturing. These environments pose challenges due to their constantly changing layouts, unpredictable obstacles, and varying conditions, which impact the performance of localization systems. This article presents a novel real-time localization scoring architecture to address these challenges by quantifying the confidence in a robot’s positioning system. The proposed localization score improves map reconciliation, manages sensor interference, adapts navigation strategies, and enhances traffic coordination. Extensive experimental studies, including real-world deployment in an operational automotive production factory, demonstrate the robustness, accuracy, and adaptability of the developed localization score algorithm. The results showcase its potential to significantly enhance the operational efficiency and reliability of AMRs in industrial settings. For more about this article see link below. https://ieeexplore.ieee.org/document/11078360/a> For the open access PDF link of this article please click here . The post Real-Time Localization Scoring for Challenging Industrial Environments: Practical Experiments With Bluepath Robotics appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Zhanwei Wang, Huaijin Chen, Hendrik Cools, Bram Vanderborght, Seppe Terryn
Abstract: While most soft pneumatic grippers that operate with a single control parameter (such as pressure or airflow) are limited to a single grasping modality, this article introduces a new method for incorporating multiple grasping modalities into vacuum-driven soft grippers. This is achieved by combining stiffness manipulation with a bistable mechanism. The system features a bistable dome structure with a central suction cup and a set of vacuum bending actuators. Designed and optimized using fluid and structure modeling in finite element analysis, it offers three grasping modes: two reflex mechanisms (force- and contact-triggered) and one with active control. All modes rely on the structural buckling of the bistable dome, but differ in how this snap behavior is activated, by force, contact, or active control. Adjusting the airflow tunes the energy barrier of the bistable mechanism, enabling changes in triggering sensitivity and allowing swift transitions between grasping modes. This results in an exceptional versatile gripper, capable of handling a diverse range of objects with varying sizes, shapes, stiffness, and roughness, controlled by a single parameter, airflow, and its interaction with objects. For more about this article see link below. https://ieeexplore.ieee.org/document/10887392/a> For the open access PDF link of this article please click here . The post Integrating Software-Less Reflex Mechanisms Into Soft Robots and a Versatile Gripper: A New Bistable Method appeared first on IEEE Robotics & Automation Magazine .
2026-06-29 · Features
Lisbeth Mena, Seppe Terryn, Bram Vanderborght, Concepción A. Monje
Abstract: Modular designs in soft robots enable repair and reconfiguration, making soft modular robots suitable for applications where resilience, flexibility, and adaptability are critical. This paper introduces a modular soft robot (MSR) based on origami actuator modules that are manufactured from reversible polymers, e.g., self-healing polymers. This work highlights three key innovations enabled by reversible polymers for MSRs. First, their reversible bonding capacity can be utilized to create high-strength interfaces between modules relying on strong covalent bonds. These interfaces can bond and debond on demand through temperature control. This reversible joining principle is downscalable and enables reconfiguration. Second, their reversible crosslinks allow for origami-based manufacturing in the solid state, involving sequential folding and binding. This process transforms 2D structures into covalently bonded and airtight 3D structures. Finally, these reversible bonds introduce a self-healing capacity to the MSRs, enabling recovery from macroscopic damages. All of these innovations are demonstrated experimentally on modular vacuum origami-based actuator modules, showing successful self-healing and reconfiguration capabilities. For more about this article see link below. https://ieeexplore.ieee.org/document/10876587/a> For the open access PDF link of this article please click here . The post Reconfigurable Modular Soft Actuator Using Origami Structures With Self-Healing Materials: Several Technological Opportunities for Robotic Applications appeared first on IEEE Robotics & Automation Magazine .