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An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett–Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.

In recent years, legged (quadruped) robots have been subject of technological study and continuous development. These robots have a leading role in applications that require high mobility skills in complex terrain, as is the case of Search and Rescue (SAR). These robots stand out for their ability to adapt to different terrains, overcome obstacles and move within unstructured environments. Most of the implementations recently developed are focused on data collecting with sensors, such as lidar or cameras. This work seeks to integrate a 6DoF arm manipulator to the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree to perform manipulation tasks in SAR environments. The main contribution of this work is focused on the High-level control of the robotic set (Legged + Manipulator) using Mixed-Reality (MR). An optimization phase of the robotic set workspace has been previously developed in Matlab for the implementation, as well as a simulation phase in Gazebo to verify the dynamic functionality of the set in reconstructed environments. The first and second generation of Hololens glasses have been used and contrasted with a conventional interface to develop the MR control part of the proposed method. Manipulations of first aid equipment have been carried out to evaluate the proposed method. The main results show that the proposed method allows better control of the robotic set than conventional interfaces, improving the operator efficiency in performing robotic handling tasks and increasing confidence in decision-making. On the other hand, Hololens 2 showed a better user experience concerning graphics and latency time.

The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.

Simulation has proven to be a highly effective tool for validating autonomous systems while lowering cost and increasing safety. Currently, several dedicated simulation environments exist, but they are limited in terms of environment size, visual quality, and feature sets. As a result, many researchers have begun to consider repurposing game engines as simulators to take advantage of their greater flexibility, scalability, and graphical fidelity. This study investigates a robotics simulation environment based on the Unity3D game engine and Robot Operating System (ROS) middleware, collectively referred to as ROS-Unity3D, and compares it to the popular ROS-Gazebo robotics simulation environment. They are compared in terms of their architecture, environment creation process, resource usage, and accuracy while simulating an autonomous ground robot in real-time. Overall, with its variety of supported file types and powerful scripting interface for creating custom functionality, ROS-Unity3D is found to be a viable alternative to ROS-Gazebo. Test results indicate that ROS-Unity3D scales better to larger environments, has higher shadow quality, achieves the same or better visual-based SLAM performance, and is more capable of real-time LiDAR simulation than ROS-Gazebo. As for its advantages over ROS-Unity3D, ROS-Gazebo has a more streamlined interface between ROS and Gazebo, has more existing sensor plugins, and is more computer resource efficient for simulating small environments.

This work presents a new formulation to holistically control four cooperative multi-rotor drones controlled in two pairs. This approach uses a modular relative Jacobian with components consisting of the Jacobians of each individual drone. This type of controller relies mainly on the relative motion between the drones, consequently releasing unnecessary constraints inherent to the control of drones in absolute motion. We present the derivations of all the necessary equations of the modular relative Jacobian to control the four multi-rotor drones. We also present the derivations of the Jacobian for each drone. We implement our proposed method in the Gazebo RotorS simulation using four hexa-rotor drones modeled from Ascending Technologies called firefly drones. We present the simulation results and analyze them to show the effectiveness of our proposed approach.

Electrical power substations are visually complex and safety-critical environments with restricted access and highly variable lighting; a digital twin (DT) framework provides a controlled and repeatable context for developing and validating vision-based inspections. This paper presents a novel sensor-centric DT framework that combines accurate 3D substation geometry with physically based lighting dynamics (realistic diurnal variation, interactive sun-pose control) and representative optical imperfections. A Render-In-The-Loop (RITL) pipeline generates synthetic datasets with configurable sensor models, variable lighting, and time-dependent material responses, including dynamic object properties. A representative case study evaluates how well the framework reproduces the typical perceptual challenges of substation inspection, and the results indicate strong potential to support the development, testing, and benchmarking of robotic perception algorithms in large-scale, complex environments. This research is useful to utility operators and asset management teams, robotics/computer vision researchers, and inspection and sensor platform vendors by enabling the generation of reproducible datasets, benchmarking, and pre-deployment testing.

Autonomous underwater vehicles (AUVs) tasked with seafloor coverage require a robust integration of path planning and control strategies to operate in adverse real-world environments including obstacles, disturbances, and physical constraints. In this work, we present a fully integrated framework that combines an optimal coverage path planning approach with a robust constrained control algorithm. The path planner leverages a priori information of the target area to achieve maximal coverage, minimize path turns, and ensure obstacle avoidance. On the control side, we employ a reference modification technique that guarantees prescribed waypoint tracking performance under input constraints. The resulting integrated solution is validated in a high-fidelity simulation environment employing ROS, Gazebo, and ArduSub Software-in-the-Loop (SITL) on a BlueROV2 platform. The simulation results demonstrate the synergy between path planning and control, illustrating the framework’s effectiveness and readiness for practical seafloor operations such as underwater debris detection.

While sonar sensors are crucial for underwater robotics perception, the key challenge lies in traditional multibeam sonar simulation’s lack of comprehensive physics-based interaction models. Such missing physical aspects lead to sonar imagery discrepancies, such as the absence of coherent imaging systems and speckle noise effects exposing risks of over-fitted control designs of the systems using the sonar perceptions. Previous research addressed this gap by introducing a physics-based simulation approach by direct calculation of the point-scattering model equations from perception data obtained from rasterization. However, the raster-based method could not control the resolution of data to pipeline into image generation, and its limitation was explicitly presented in local search scenarios where the distance between data is large. To eliminate those limitations and extend capabilities without losing the quality of the image, this paper introduces a ray-based approach to replace the raster-based method when obtaining the perception data from the simulated world to pipeline into physical equation calculations. The results of the ray-based and raster-based models are compared for the front floating object and the ground grazing local search scenario to confirm that the ray-based method maintains equal quality of sonar image generation, including physical characteristics, but it has more flexibility and capability in control of data resolution for correct sonar image generation.