Robot Navigation Research Articles

A generalized framework for identification and geometrical feature extraction of circular and polygonal shaped prisms

Abstract This paper introduces a versatile framework crucial for robotic applications such as object manipulation, mobile robot navigation, and pole climbing. It addresses the identification of geometric shapes and dimensions of diverse objects found in varied environments. The proposed method utilizes LiDAR scanning to capture objects from different angles, generating point clouds merged through transformations and superimpositions. After filtering and slicing, intersections are isolated and projected onto a chosen datum plane. The framework employs Non-Linear Least Square fitting via Gauss Newton iterative approach, utilizing pseudo-inverse Jacobian of a hypotrochoid to approximate polygons. The algorithm consecutively fits polygon prisms, determining the best fit with the least norm of error. Results indicate an average least square error of less than 9% for radius fitting and a high f-score for shape identification.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

Accuracy of Zygomatic Implant Placement Using Task-Autonomous Robotic System or Dynamic Navigation: An InVitro Study.

To evaluate and compare the accuracy of task-autonomous robot-assisted implant surgery (RAIS) and dynamic computer-assisted implant surgery (dCAIS) for zygomatic implant placement. Ten atrophic edentulous maxilla models requiring zygomatic implant (ZI) placement were randomly divided into the RAIS and dCAIS groups. Osteotomies and implant placement were performed under the guidance of a task-autonomous robotic system or dynamic navigation system. A total of 20 ZIs were analyzed. The angular, coronal, lateral coronal, coronal depth, apical, lateral apical, and apical depth deviations were measured and analyzed between the two groups. The primary outcome parameters were the angular deviations between the planned and the placed ZIs. Data was subjected to descriptive and comparative statistical analysis. The significance of inter-group differences for continuous variables was assessed with Student's two-sample t-tests, Welch two-sample t-tests, and Mann-Whitney U tests according to the distribution normality and variance homogeneity. ZI placement deviations were compared between the RAIS and dCAIS groups, showing a mean angular deviation of 0.92 ± 0.40° versus 2.03 ± 0.53° (p < 0.001), a mean (±SD) coronal deviation of 0.48 ± 0.25 mm versus 1.29 ± 0.46 mm (p < 0.001), and a mean apical deviation of 0.88 ± 0.28 mm versus 1.96 ± 0.46 mm (p < 0.001). For computer-guided ZI placement, task-autonomous RAIS was superior to dCAIS in terms of accuracy.

Advancements in amphibious robot navigation through wheeled odometer uncertainty extension and distributed information fusion

The advancement and safeguarding of the water-land interface region is of paramount importance, and amphibious robots with the capacity for autonomous operation can play a pivotal role in this domain. However, the inability of the majority of reliable navigation sensors to adapt to the water-land interface environment presents a significant challenge for amphibious robots, as obtaining positional information is crucial for autonomous operation. To address this issue, we have proposed a positioning and navigation framework, designated as NAWR (Navigation Algorithm for Amphibious Wheeled Robots), with the objective of enhancing the navigation capabilities of amphibious robots. Firstly, a method for representing the odometer's confidence based on a simplified wheel-terrain interaction model has been developed. This method quantitatively assesses the reliability of each odometer by estimating the slip rate. Secondly, we have introduced an improved split covariance intersection filter (I-SCIF), which maximizes the utilization of navigation information sources to enhance the accuracy of positional estimation. Finally, we will integrate these two methods to form the NAWR framework and validate the effectiveness of the proposed methods through multiple robot field trials. The results from both field trials and ablation tests collectively demonstrate that the modules and overall approach within the NAWR framework effectively enhance the navigation capabilities of amphibious robots.

THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.

An Automatic Mapping Method of Navigation Map for Outdoor Road Scenes

Abstract. In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

Towards full autonomy in mobile robot navigation and manipulation

AbstractRobot autonomy is a key for automatizing industrial production plants, facilitating responsive search and rescue (SAR), and for interacting in urban settings. This work presents mobile systems capable of carrying out these tasks and summarises experience from practical deployments in three different domains for navigation. For industry, a mobile manipulator is presented, which is used for dynamic precise docking and object handling. For SAR, a semi-autonomous system is presented which is used for sampling hazardous materials, manipulating tools, and for localizing nuclear radiation sources. To improve urban logistics, a mobile robot autonomously navigates complex outdoor environments using semantic mapping techniques. Future work will explore further advancements that strive toward increased flexibility, reasoning and universal applicability of mobile robots. The goal is to reduce training, onboarding and tool changing times to reduce manual interfacing times.

Pixel-wise matching cost function for robust light field depth estimation

Depth estimation has significant potential in industrial applications, such as robot navigation. Light Field (LF) technology captures both spatial and angular information of a scene, enabling precise depth acquisition. Stereo matching technology has been commonly used for this task in LF imaging. However, previous methods typically compute the matching cost by simply shifting images with predefined offsets, causing poor performance in regions with sudden depth distribution changes (e.g., edges). To address this issue, we propose a Pixel-wise Matching Cost Function (PMCF) to estimate depth in units of pixel, then design a pixel-based network, termed PixelNet, for depth prediction, which achieves top rankings on the HCI 4D light field benchmark. Specifically, our cost function consists of two modules: the range search strategy and the modulation mechanism. The range search strategy enables the network to iteratively optimize depth on a per-pixel basis, while the modulation mechanism effectively handles scene noise and occlusions. By integrating these two modules, our function enables the generation of depth maps with sharper edges and smoother surfaces, compared with previous methods. Finally, we design a pixel-based network and apply it to both synthetic and real-world scenes, demonstrating that our method outperforms state-of-the-art methods in both scenes. Furthermore, PixelNet can also function as a post-processing network that can be seamlessly integrated with existing methods for depth refinement.

A Real-Time Semantic Map Production System for Indoor Robot Navigation.

Although grid maps help mobile robots navigate in indoor environments, some lack semantic information that would allow the robot to perform advanced autonomous tasks. In this paper, a semantic map production system is proposed to facilitate indoor mobile robot navigation tasks. The developed system is based on the employment of LiDAR technology and a vision-based system to obtain a semantic map with rich information, and it has been validated using the robot operating system (ROS) and you only look once (YOLO) v3 object detection model in simulation experiments conducted in indoor environments, adopting low-cost, -size, and -memory computers for increased accessibility. The obtained results are efficient in terms of object recognition accuracy, object localization error, and semantic map production precision, with an average map construction accuracy of 78.86%.

A Robot Navigation System Based on Improved A-Star Algorithm

A Robot Navigation System Based on Improved A-Star Algorithm

An Enhanced Particle Filtering Method Leveraging Particle Swarm Optimization for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

An Enhanced Particle Filtering Method Leveraging Particle Swarm Optimization for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

Practice Pattern Variations in the Use of Neuromonitoring, Image Guidance, and Robotics for Lumbar Pedicle Screw Placement Based on a Nationwide Neurosurgery Survey

Lumbar pedicle screw placement surgery involves various assistive technologies, including fluoroscopic, stereotactic, or robotic navigation and intraoperative neuromonitoring (IONM). We aimed to discern neurosurgeons' preferences for screw placement techniques and IONM utility, while also considering the influence of experience. A survey was distributed to members of the Congress of Neurological Surgeons using REDCap software, collecting demographic data and querying preferred techniques for screw placement and IONM modalities. Opinions on IONM use during stereotactic or robotic navigation were also obtained. Responses were analyzed using Pearson's Chi Square and ANOVA tests via R software. Of 188 responses, 35.5% (n=67) reported 1-10 years of experience and 64.5% (n=121) reported ≥11 years. Less experienced neurosurgeons utilized stereotactic navigation more than those with greater experience (p < 0.001). Seasoned neurosurgeons utilized fluoroscopic guidance more often (p = 0.038). Less experienced neurosurgeons employed 1.69 (±0.11) techniques for their fixation surgeries compared to 1.50 (±0.0.8) for more experienced neurosurgeons. Robotic navigation utilization was low and comparable between the groups. Surgeons employing multiple techniques utilized t-EMG the most (62.1%, p = 0.024). No strong opinions emerged on the necessity of multi-modality IONM with robotic or stereotactic navigation. This national survey shows that stereotactic navigation is the predominant technique for pedicle screw placement among less experienced neurosurgeons, with seasoned neurosurgeons leaning towards fluoroscopic guidance. Robotic guidance was the least utilized technique with no observed difference based on experience. Neurosurgeons employing multiple techniques use IONM the most, compared with surgeons who only use stereotactic navigation and/or robotic guidance.

A Cooperative and Multifunctional Magnetic Continuum Robot for Noninteractive Access, Dexterous Navigation, and Versatile Manipulation

AbstractSoft continuum robots can navigate through narrow and tortuous lumina. However, its interactions with the lumina during steering pose a risk of luminal injuries, especially if it integrates multifunctional units, which increase both the robot's size and local stiffness. Therefore, a continuum robot comprising coaxially assembled Guider and Follower components is designed that utilize phase change materials for noninteractive navigation and versatile manipulation. Navigation occurs through alternating softening‐hardening cycles: First, the Guider softens and advances under magnetic steering while the rigid Follower provides support. Then, the Guider solidifies to form a new backbone as the Follower softens to replicate its path. This process maintains structural integrity while enabling navigation along desired trajectories, thereby greatly reducing interactions with the lumen. To further accomplish complex tasks in large and open spaces, a continuum robot system with a triple‐individual Guider is developed in which the multi‐degree‐of‐freedom movement of the individuals is precisely and sequentially controlled through segmented stiffness regulation. This configuration achieves versatile manipulation, including targeted delivery, precision coating, grasping, and obstacle clearing in complex environments. Combining cooperative movement and diverse functionalities, this continuum robot design offers a new approach to dexterous navigation and intervention in minimally invasive surgical procedures.

Map Construction and Positioning Method for LiDAR SLAM-Based Navigation of an Agricultural Field Inspection Robot

In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

DGRO: Doppler Velocity and Gyroscope-Aided Radar Odometry

A stable and robust odometry system is essential for autonomous robot navigation. The 4D millimeter-wave radar, known for its resilience in harsh weather conditions, has attracted considerable attention. As the latest generation of FMCW radar, 4D millimeter-wave radar provides point clouds with both position and Doppler velocity information. However, the increased uncertainty and noise in 4D radar point clouds pose challenges that prevent the direct application of LiDAR-based SLAM algorithms. To address this, we propose a SLAM framework that fuses 4D radar data with gyroscope readings using graph optimization techniques. Initially, Doppler velocity is employed to estimate the radar’s ego velocity, with dynamic points being removed accordingly. Building on this, we introduce a pre-integration factor that combines ego-velocity and gyroscope data. Additionally, leveraging the stable RCS characteristics of radar, we design a corresponding point selection method based on normal direction and propose a scan-to-submap point cloud registration technique weighted by RCS intensity. Finally, we validate the reliability and localization accuracy of our framework using both our own dataset and the NTU dataset. Experimental results show that the proposed DGRO system outperforms traditional 4D radar odometry methods, especially in environments with slow speeds and fewer dynamic objects.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ОБРАЗОВАНИИ

The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.

Efficient 3D robotic mapping and navigation method in complex construction environments

AbstractRecent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach

The paper presents a Fuzzy Q-Learning (FQL) and optical flow based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.

Addressing unpredictable movements of dynamic obstacles with deep reinforcement learning to ensure safe navigation for omni-wheeled mobile robot

The safe and efficient navigation of mobile robots in the presence of unknown dynamic obstacles remains a complex and unresolved challenge. This paper presents collision-free path planning for a mobile robot that safely deals with multi-directional obstacles, that is, randomly moving dynamic obstacles, using a Deep Reinforcement Learning (DRL) algorithm named Deep Q-Network (DQN) with inflated robot reward functions. The robot moves in a time-efficient and collision-free route while maintaining a safe distance with both static and unpredictable dynamic obstacles. The modified DQN algorithm takes RGB images of the environment as input for training a Convolution Neural Network (CNN) and provides a safe and short path for navigation. The robot used for training is an omni-wheeled mobile robot exploring outdoors, that is, concourse environment, and indoors, that is, home environment. The Closed-Loop Inverse Kinematics (CLIK) algorithm is employed to control a mobile robot to follow the desired path. The simulation results indicate that the proposed algorithm with inflated robot reward functions demonstrates remarkable performance as compared to recently used Reinforcement Learning (RL) algorithms when dealing with both stationary and randomly moving obstacles in the given environment.

Robotic-Assisted Navigation in Single-Level Transforaminal Lumbar Interbody Fusion Reduces Surgeons’ Mental Workload Compared With Fluoroscopic and Computed Tomographic Techniques: A Nonrandomized Prospective Controlled Trial

Background: Pedicle screw placement during spine fusion is physically and mentally demanding for surgeons. Consequently, spine surgeons can become fatigued, which has implications for both patient safety and surgeon well-being. Purpose: We sought to assess the cognitive workload of surgeons placing pedicle screws using robotic-assisted navigation compared with fluoroscopic and computed tomography (CT)-assisted placement. Methods: We performed a nonrandomized prospective controlled trial to compare the cognitive workload of 3 surgeons performing single-level minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) using robotic, CT, or fluoroscopic navigation on 25 patients (15 in the robotic navigation group and 10 in the nonrobotic group). Immediately after each procedure, surgeons submitted the National Aeronautics and Space Administration–Task Load Index (NASA-TLX), which has 6 subscales: mental demands, physical demands, temporal demands, performance, effort, and frustration. Four tasks associated with pedicle screw placement were assessed independently: (1) screw planning, (2) calibrating robot/obtaining imaging/registration, (3) pedicle cannulation, and (4) screw placement. Patient demographics and surgical characteristics were obtained and reviewed. Results: Surgeons’ self-reported cognitive workload was significantly reduced when using robotic-assisted navigation versus CT/fluoroscopic navigation. Workload was reduced for screw planning, pedicle cannulation, and screw placement. In addition, there were significant reductions in each subdomain for these 3 tasks, encompassing mental demand, physical demand, temporal demand, effort, and frustration with improved task performance. Conclusions: This study found significant reductions in mental workload with improved perceived performance for robotic-assisted pedicle screw placement compared with fluoroscopic and CT-navigation techniques. Lowering the cognitive burden associated with screw placement may allow surgeons to address the remainder of the operative case with less decision fatigue, prevent complications, and increase surgeon wellness.