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Topic Review
3D Measurement of High-Reflective Surfaces
The reflection phenomenon exhibited by highly reflective surfaces considerably affects the quality of captured images, thereby rendering the task of structured light (SL) 3D reconstruction.
  • 397
  • 06 Jul 2023
Topic Review
Robot Programming Skill Assessment
Robot programming skill classes are becoming more popular. Higher order thinking, on the other hand, is an important issue in developing the skills of 21st-century learners. Truth be told, those two abilities are consistent subjects that are trending in academics.
  • 378
  • 19 May 2022
Topic Review
Classification for Monocular RGB 3D Reconstruction Systems
Pure monocular 3D reconstruction is a complex problem that has attracted the research community’s interest due to the affordability and availability of RGB sensors. Simultaneous Landing and Mapping (SLAM), Visual Odometry (VO), and Structure from Motion (SFM) are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion. As a complex problem, pure visual monocular 3D reconstruction has been addressed from multiple perspectives combining various techniques that can be classified following different approaches. A better approach to classify monocular RGB 3D reconstruction systems is the taxonomy, considering three classifications covering dense, sparse, direct, indirect, classic, and machine learning-based proposals.
  • 375
  • 22 Aug 2023
Topic Review
Educational Robotics Program Impacts in Early Childhood
Engaging educational programs that involve science, technology, engineering and mathematics (STEM) with young children are of critical importance because such a curriculum is often underdeveloped in early childhood education. Robotics education remains relatively sparse for the youngest of learners; however, several robotics programs have been implemented in school settings, and research is emerging measuring their effectiveness and impact.
  • 374
  • 04 Dec 2023
Topic Review
Multi-Robot Exploration and Optimization Methods
Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This entry proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. The researchers compared HVCME with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.
  • 369
  • 20 Nov 2023
Topic Review
Care of the Elderly and Robots in Healthcare
The use of robots in elderly care represents a dynamic field of study aimed at meeting the growing demand for home-based health care services. The application of robots in elderly home care is examined and contributes to the literature by introducing a comprehensive and functional architecture within the realm of the Internet of Robotic Things (IoRT). This architecture amalgamates robots, sensors, and Artificial Intelligence (AI) to monitor the health status of the elderly. 
  • 365
  • 22 Sep 2023
Topic Review
Simultaneous Localization and Mapping System in Dynamic Environment
Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to dynamic real-world environments. The correctness of re-localization and recall of loop closure detection are both lower when the mobile robot loses frames in a dynamic environment.
  • 362
  • 20 Nov 2023
Topic Review
Urban Scene Reconstruction via Neural Radiance Fields
3D reconstruction of urban scenes is an important research topic in remote sensing. Neural Radiance Fields (NeRFs) offer an efficient solution for both structure recovery and novel view synthesis. The realistic 3D urban models generated by NeRFs have potential future applications in simulation for autonomous driving, as well as in Augmented and Virtual Reality (AR/VR) experiences.
  • 355
  • 10 Nov 2023
Topic Review
3D Object Detection Methods
Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space.
  • 355
  • 27 Nov 2023
Topic Review
Three-Dimensional Flight Corridor for Unmanned Aerial Vehicle
The 3D flight corridor is established as a topological structure based on a hand-crafted path either derived from a computer-generated environment or provided by the human operator, which captures humans’ preferences and desired flight intentions for the given space. This corridor is formulated as a series of interconnected overlapping convex polyhedra bounded by the perceived environmental geometries, which facilitates the generation of suitable 3D flight paths/trajectories that avoid local minima within the corridor boundaries. An occupancy check algorithm is employed to reduce the search space needed to identify 3D obstacle-free spaces in which their constructed polyhedron geometries are replaced with alternate convex polyhedra. 
  • 351
  • 16 Oct 2023
Topic Review
Robot Arm Reaching Based on Inner Rehearsal
Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. The researchers propose a robot arm motion control method based on inner rehearsal. Inspired by the cognitive mechanism of inner rehearsal observed in humans, this approach allows the robot to predict or evaluate the outcomes of motion commands before execution. By enhancing the learning efficiency of models and reducing excessive physical executions, the method aims to improve robot arm reaching across different platforms.
  • 342
  • 08 Nov 2023
Topic Review
Implicit Shape Model Trees
“Active Scene Recognition” (ASR) is an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. “Implicit Shape Model (ISM) trees” are proposed as the scene model underpinning the ASR approach. These trees are a hierarchical model of multiple interconnected ISMs.
  • 341
  • 04 Dec 2023
Topic Review
Human Decision Making in Human–Robot Collaboration
The advent of Industry 4.0 has heralded advancements in human–robot collaboration (HRC), necessitating a deeper understanding of the factors influencing human decision making within this domain. An HRC system combines human soft skills such as decision making, intelligence, problem-solving, adaptability and flexibility with robots’ precision, repeatability, and the ability to work in dangerous environments.
  • 336
  • 22 Feb 2024
Topic Review
Swarm Exploration and Communications
Swarm exploration by multi-agent systems relies on stable inter-agent communication. However, so far both exploration and communication have been mainly considered separately despite their strong inter-dependency in such systems. By a semantic communication design, communication efficiency in terms of latency, required data rate, energy, and complexity may be improved. 
  • 322
  • 28 Jul 2023
Topic Review
Depth Estimation in Structured Driving Scenes
Depth estimation is an important part of the perception system in autonomous driving. Studies often reconstruct dense depth maps from RGB images and sparse depth maps obtained from other sensors. However, existing methods often pay insufficient attention to latent semantic information. Considering the highly structured characteristics of driving scenes, the researchers propose a dual-branch network to predict dense depth maps by fusing radar and RGB images. The driving scene is divided into three parts in the proposed architecture, each predicting a depth map, which is finally merged into one by implementing the fusion strategy in order to make full use of the potential semantic information in the driving scene.
  • 313
  • 22 Sep 2023
Topic Review
Multi-Eye to Robot Indoor Calibration Dataset
The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, researchers obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.
  • 306
  • 09 Jun 2023
Topic Review
Robotic Applications in Maintenance, Repair, and Overhaul Hangar
The aerospace industry has continually evolved to guarantee the safety and reliability of aircraft to make air travel one of the safest and most reliable means of transportation. Mobile robots encompass comprehensive system structures that work together through perception, detection, motion planning, and control. The subject of autonomous robot navigation entails mapping, localisation, obstacle detection, avoidance, and achieving an optimal path from a starting point to a predefined target location efficiently.
  • 303
  • 06 Mar 2024
Topic Review
Risk Determination versus Risk Perception
Researchers review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; the DoD’s assessment was rushed, suppressing alternative risk perceptions.
  • 287
  • 21 Jun 2023
Topic Review
Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations.
  • 284
  • 20 Jun 2023
Topic Review
LiDAR Local Domain Adaptation for Autonomous Vehicles
Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model’s performance. Domain adaptation is a form of transfer learning that aims to minimize the domain shift between datasets.
  • 279
  • 05 Jan 2024
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