Search Results (162)

Multi-agent coordination with task allocation, routing, and scheduling presents critical challenges when deploying heterogeneous robotic systems in constrained agricultural environments. These systems involve real-time sensing during their operations with various sensors, and having quick updates on coordination based on sensed data is critical. This paper addresses the specific requirements of harvesting automation through three heuristic approaches: (1) primal–dual workload balancing inspired by combinatorial optimization techniques, (2) greedy task assignment with iterative local optimization, and (3) LLM-based constraint processing through prompt engineering. Our agricultural application scenario incorporates robot size constraints for navigating narrow crop rows while optimizing task completion time. The greedy heuristic employs rapid initial task allocation based on proximity and capability matching, followed by iterative route refinement. The primal–dual approach adapts combinatorial optimization principles from recent multi-depot routing solutions, dynamically redistributing workloads between robots through dual variable adjustments to minimize maximum completion time. The LLM-based method utilizes structured prompt engineering to encode spatial constraints and robot capabilities, generating feasible solutions through successive refinement cycles. We implemented and compared these approaches through extensive simulations. Preliminary results demonstrate that all three approaches produce feasible solutions with reasonable quality. The results demonstrate the potential of the methods for real-world applications that can be quickly adopted into variations of the problem to offer valuable insights into solving complex coordination problems with heterogeneous multi-robot systems. Full article

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms. Full article

Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safety limitations of existing diagnostic robots in factory environments, a hierarchical fuzzy logic-based navigation and obstacle avoidance algorithm is proposed in this study. The algorithm is constructed based on zero-order Takagi–Sugeno type fuzzy control, comprising subfunctions for navigation, static obstacle avoidance, and dynamic obstacle avoidance. Coordinated navigation and equipment protection are achieved by jointly considering the information of the fault point and surrounding equipment. The concept of a dynamic safety boundary is introduced, wherein the normalized breached level is used to replace the traditional distance-based input. In the inference process for dynamic obstacle avoidance, the relative speed direction is additionally considered. A Mamdani-type fuzzy inference system is employed to infer the necessity of obstacle avoidance and determine the priority target for avoidance, thereby enabling multi-objective planning. Simulation results demonstrate that the proposed algorithm can guide the diagnostic robot to within 30 cm of the fault point while ensuring collision avoidance with both equipment and obstacles, enhancing the completeness and safety of the fault point searching process. Full article

In this study, a vision-based remote and synchronized control scheme is proposed for the double six-DOF robotic manipulators. Using an Intel RealSense D435 depth camera and MediaPipe skeletal image feature technique, the operator’s 3D hand pose is captured and mapped to the robot’s workspace via coordinate transformation. Inverse kinematics is then applied to compute the necessary joint angles for synchronized motion control. Implemented on double robotic manipulators with the MoveIt framework, the system successfully achieves a collaborative teleoperation control task to transfer an object from a robotic manipulator to another one. Further, moving average filtering techniques are used to enhance trajectory smoothness and stability. The framework demonstrates the feasibility and effectiveness of non-contact, vision-guided multi-robot control for applications in teleoperation, smart manufacturing, and education. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

Research on extreme environmental exploration using unmanned robots has recently attracted significant attention. In particular, unmanned robot exploration in vast areas such as Antarctica requires a system capable of remotely monitoring and controlling multiple robots. This paper proposes an integrated control system designed to monitor, control, and assign exploration tasks to multiple robots operating in extreme environments. This system utilizes GPS-based collaboration to support specific tasks, such as crevasse exploration and automatic battery charging, in Antarctic target areas. The system’s user interface (UI) is designed for efficiency and integrates elements such as remote control and mission execution commands tailored to the Antarctic environment. The proposed system was implemented using three robot platforms, and through performance evaluation tests in Antarctica, it achieved a cumulative driving distance of over 500 km and over 200 h of operation for over a month. The successful execution of simultaneous crevasse exploration by three robots highlights the system’s capability for coordinated multi-robot operations in extreme environments. Full article

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article

This paper presents the design and implementation of a mobile robotic platform modelled as a layered Cyber–Physical System (CPS). Inspired by architectures commonly used in industrial Distributed Control Systems (DCSs) and large-scale scientific infrastructures, the proposed system incorporates modular hardware, distributed embedded control, and multi-level coordination. The robotic platform, named MapBot, is structured according to a five-layer CPS model encompassing component, control, coordination, supervisory, and management layers. This structure facilitates modular development, system scalability, and integration of advanced features such as a digital twin. The platform is implemented using embedded computing elements, diverse sensors, and communication protocols including Ethernet and I2C. The system operates within the ROS2 framework, supporting flexible task distribution across processing nodes. As a use case, two localization techniques—Adaptive Monte Carlo Localization (AMCL) and pose graph SLAM—are deployed and evaluated, highlighting the performance trade-offs in map quality, update frequency, and computational load. The results demonstrate that CPS-based design principles offer clear advantages for robotic platforms in terms of modularity, maintainability, and real-time integration. The proposed approach can be generalised for other robotic or mechatronic systems requiring structured, layered control and embedded intelligence. Full article

Formation control plays a vital role in coordinating multi-agent systems and swarm robotics, enabling collaboration in applications such as autonomous vehicles, robotic swarms, and distributed sensing. This paper introduces the formation-control problem, highlights its challenges, and compares centralized and decentralized schemes. We review recent advances and analyze popular algorithms, then propose a hybrid framework that combines leader–follower tracking with an artificial potential field (APF) safety layer. In three-UAV tests, the followers cross paths and one encounters a static obstacle. We run multiple simulations across scenarios with obstacles and varying formations. Results show the hybrid controller maintains the required formation while avoiding inter-agent collisions. Using quantitative metrics, we find the leader–follower baseline achieves the lowest formation error but has the most safety violations, whereas APF greatly improves safety at the cost of higher error. The hybrid combines these strengths—delivering APF-level safety with lower error and negligible runtime overhead—providing a practical balance between precise formation keeping and robust collision avoidance. Full article

Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization method that enhances the reliability of localization outputs without modifying the prediction model itself. This study introduces a percentile-based rejection strategy that filters out unreliable 3-degree-of-freedom pose predictions based on aleatoric and epistemic uncertainties the network estimates. We apply this approach to a multi-modal end-to-end localization that fuses RGB images and 2D LiDAR data, and we evaluate it across three real-world datasets collected using a commercialized serving robot. Experimental results show that applying stricter uncertainty thresholds consistently improves pose accuracy. Specifically, the mean position error, calculated as the average Euclidean distance between the predicted and ground-truth (x, y) coordinates, is reduced by 41.0%, 56.7%, and 69.4%, and the mean orientation error, representing the average angular deviation between the predicted and ground-truth yaw angles, is reduced by 55.6%, 65.7%, and 73.3%, when percentile thresholds of 90%, 80%, and 70% are applied, respectively. Furthermore, the rejection strategy effectively removes extreme outliers, resulting in better alignment with ground truth trajectories. To the best of our knowledge, this is the first study to quantitatively demonstrate the benefits of percentile-based uncertainty rejection in multi-modal and end-to-end localization tasks. Our approach provides a practical means to enhance the reliability and accuracy of localization systems in real-world deployments. Full article

Real-time coordination of heterogeneous multi-agent systems in dynamic and partially observable environments poses significant challenges. To address this, we propose a framework that integrates fuzzy inference systems with real-valued genetic algorithms to optimize decision-making under strict time constraints and sensory uncertainty. We evaluate the proposed method in the RoboCup Soccer Simulation 2D League, where 22 autonomous agents coordinate through a fuzzy-evaluated action sequence search. Spatial heuristics are encoded as fuzzy rules, and optimization based on genetic algorithms refines evaluation function parameters according to performance metrics such as number of shots, goal area entries, and scoring rates. The resulting control strategy remains interpretable; spatial heat maps reveal emergent behaviors such as coordinated positioning and ridgeline passing patterns near the penalty area. The experiments against established RoboCup teams, serving as benchmarks, demonstrate the competitive performance of our trained agents while enabling analyses of evolving decision structures and agent behaviors. Our method provides a transparent and adaptable framework for controlling heterogeneous agents in uncertain real-time environments, with broad applicability to robotics, autonomous systems, and distributed control systems. Full article

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

ROS (Robot Operating System) has become the de facto standard in robotics research and development, with ROS 2, in particular, offering enhanced support for real-time communication, distributed systems, and scalable multi-robot applications. These capabilities have driven its widespread adoption across academia, industry, and the open-source community. However, deploying ROS 2 applications across heterogeneous hardware platforms remains a complex task—especially in scenarios that require tightly coordinated, multi-agent systems. In such cases, the failure of a single agent can propagate disruptions throughout the system. A representative example is Ultra-wideband (UWB)-based multi-robot relative localization, where inter-robot dependencies are essential for maintaining accurate relative positioning. While Kubernetes offers powerful features for automated deployment and orchestration, its integration with ROS 2 has not yet been thoroughly evaluated within the context of specific robotic applications. This paper addresses this gap by integrating Kubernetes with ROS 2 in a UWB-based multi-robot localization system, using UWB ranging error mitigation as a representative application. An edge cluster comprising five NVIDIA Jetson Nano devices and one laptop is orchestrated using Kubernetes, with a Jetson Nano node mounted on each robot. We deploy Long Short-Term Memory (LSTM)-based error mitigation modules on the edge nodes and systematically induce failures in various combinations of these modules. The system’s resilience and robustness are then assessed by analyzing position errors under different failure scenarios. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article