Mobile Robot Research Articles

Auxetic and Holonomic Mobile Robot for Enhanced Navigation in Constrained Terrains

ABSTRACTMobile robots, also known as field robots, perform various tasks practically and effectively on behalf of humans, particularly in inaccessible and/or hazardous environments. In recent years, as the underlying technology has matured remarkably, from hardware to algorithms, the applicability and specialization of mobile robots have gradually expanded. However, robots still present a poor workspace when facing terrains narrower than their size. To address this drawback, we propose a novel mobile robot specialized for operating on narrow terrains, which we call a negative Poisson's ratio robot. Its features include an auxetic body and holonomic locomotion. An auxetic body is a structure based on the theory of a negative Poisson's ratio, in which the lateral width of the robot body decreases as the longitudinal length decreases. This structure enables collision‐free deformation during contraction. The deformability ratio of the auxetic body was 5.13% for the longitudinal length and 30.63% for the lateral width. Holonomic locomotion enables a robot to drive omnidirectionally, and allows the robot to be controlled with a simple, direct, and single command without path planning. This was implemented using Mecanum wheels. To substantiate the efficacy of a robot with a negative Poisson's ratio, we conducted maneuverability experiments including various narrow terrains and path shapes. The proposed robot achieved sufficient maneuverability performance, even in narrow terrains, and outperformed other same‐sized robots. Supporting materials, such as experimental videos, can be accessed on the following website: http://irobot.kumoh.ac.kr/Size-Adjustable-Mobile-Robot.

Expanding mobile robot applications in care environments: client perspectives for added guidance

Expanding mobile robot applications in care environments: client perspectives for added guidance

Robust Trajectory Tracking Control for Multiple Mobile Robots

This paper addresses the robust trajectory tracking control challenge for multiple mobile robots in complex environments, an increasingly critical issue as the number of robots grows and the demand for high tracking accuracy and efficiency increases. Existing methods are unable to strike a balance between safety and tracking precision in multi-robot trajectory tracking, with the requirement that robots should be as close as possible to their designated positions at all times during tracking. To bridge these gaps, we introduce Multi Mobile Robot Trajectory Model Predictive Control (MMRT-MPC) and the Trajectory Action Dependence Graph (TADG) framework. MMRT-MPC incorporates multiple indicators into the cost function to improve trajectory tracking accuracy and efficiency. Meanwhile, TADG ensures safety during trajectory tracking and is compatible with MMRT-MPC as well as other control algorithms. Simulations in Gazebo show that the TADG method ensures the safety of trajectory tracking control. Compared with applying TADG to Prioritized Trajectory Optimization (PTO) and Bellman Dynamic Programming with Model Predictive Control (BDP-MPC), MMRT-MPC+TADG reduces average delay by 17.7% and 11.6% respectively under different numbers of robots, and by 20.8% and 14.3% in the case of 30 robots with random delays added. Furthermore, the validity of our proposed method is confirmed through real-world experimental results.

A Hybrid Optimization Algorithm of ACO and RRT for Solving Mobile Robot Path Planning

Abstract Mobile robots have become a focal point in various industries due to their advancements in intelligence and extensive application demands, with path planning technology serving as a fundamental support for achieving autonomous navigation. Although the traditional ant colony optimization (ACO) algorithm has been widely applied to path planning, it still suffers from the tendency to fall into local optima, insufficient global search capability, and the generation of excessive redundant nodes. To address this, a hybrid path planning algorithm is proposed in this paper. The algorithm generates an initial path using the adaptive step rapidly-exploring random tree (AS-RRT-Connect), which narrows the search space and provides a more accurate pheromone distribution for the improved ant colony optimization (IACO), thereby significantly improving the convergence speed of the algorithm. Additionally, four improvement strategies are designed, including an enhanced state transition rule, a path crossover strategy, a pheromone update method, and a local optimization strategy, to further enhance the path planning performance of differential drive mobile robots. Multi-environmental experiments conducted on the Matlab and Gazebo simulation platforms confirm that notable improvements in path smoothness, convergence speed, and planning stability are achieved by the modified algorithm. Compared to the traditional ACO algorithm, a 69.6% reduction in the number of turns has been achieved, the running time has been shortened by 52.94%, the standard deviation of path length has been decreased by 65.2%, and the average path length has been reduced by 20.1%. Studies on actual vehicles have demonstrated that the AS-RRT-Connect-IACO algorithm is capable of producing smooth, ideal routes and meeting the worldwide navigation needs of four-wheel differential drive carts in real-world scenarios.

Proxemic Discomfort in Shared Spaces: The Role of Mobile Robot Behavior and Pedestrian State

As mobile robots become more common in shared public spaces, understanding how their movements affect pedestrian comfort has become a key challenge in human–robot interaction. Although prior research has investigated the effects of robot lateral distance and speed, findings have been inconsistent, with little attention paid to how these factors interact with pedestrians’ mobility states (e.g., walking vs. stopping). To address this gap, we conducted a virtual reality experiment where participants experienced 30 scenarios combining five lateral distances, three robot velocities, and two pedestrian states. Discomfort was measured using standardized questionnaires and real-time button presses. Results showed that discomfort increased at closer distances and higher speeds, with these effects amplified when pedestrians were walking. Discomfort was minimized when robots-maintained distances over 120 cm and moved slower than typical walking speed. This study offers new insights into how robot proxemics and pedestrian mobility jointly shape discomfort, informing proximity-aware navigation design.

ROS-Based Navigation and Obstacle Avoidance: A Study of Architectures, Methods, and Trends

With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems.

Path planning for mobile robots based on improved sparrow search algorithm under various maps

With the advancement of automation technology, swarm intelligence algorithms are becoming increasingly crucial for mobile robot path planning. Therefore, an improved sparrow search algorithm (CSWSSA) is proposed to address the shortcomings of swarm intelligence algorithms in path planning, such as long planning time and suboptimal planned paths. Firstly, Utilizing Elite SPM Mapping to initialize CSWSSA to improve the individual of sparrow quality and diversity. Secondly, an improved sine cosine algorithm was proposed to increase the ability to search maps and balance the ability of extensive and fine search. Then, a tracking strategy and an adaptive T-distribution strategy are adopted at different position update points to further avoid the algorithm plunging into local minimum. Finally, in order to avoid sparrow individuals exceeding their boundaries, a boundary redistribution mechanism was designed. To test the optimization and optimization ability of the proposed CSWSSA for various functions, some extensive validations are performed on publicly available test functions. Furthermore, we conducted experimental verification on both simulated and real maps, and compared our proposed method with some widely used algorithms, and the results unanimously demonstrate the superiority of the proposed CSWSSA. Compared with other methods, the optimal path length and the average path length are shortened by 18.8% and 15.4% respectively. Moreover, CSWSSA has the best stability, with a value of 1.0255. The research results of this project will provide new ideas for path planning of intelligent optimization algorithms.

Omni-Explorer: A Rapid Autonomous Exploration Framework With FOV Expansion Mechanism.

Autonomous exploration is a fundamental challenge for numerous applications of mobile robots. Traditional methods often lead to impractical and discontinuous trajectories, which may substantially deteriorate the exploration time. In this work, we propose a rapid autonomous exploration framework with a field-of-view (FOV) expansion mechanism. We present a 1-degree-of-freedom (DOF) FOV expansion mechanism, coupled with a frontier-gravitation FOV direction planning method to decouple the direction of the sensor's FOV from the robot velocity direction. Our approach includes a rapid frontier viewpoint generation method utilizing principal component analysis (PCA). Moreover, we introduce a sliding window travelling salesman problem (TSP) for global coverage path planning, incorporating an attenuation coefficient to increase the exploration priority of independent small frontiers and reduce revisit probability. Finally, compared to state-of-the-art (SOTA) approaches, our proposed mechanism and framework beneficially reduce exploration time by 30%-44% and enhance the continuity of the robot movement in both simulation and real-world scenarios.

The concept of a hybrid mobile robot

The article considers the concept of a hybrid mobile robot that combines the capabilities of a walking platform and a quadcopter. The proposed system provides effective adaptation to complex terrain and the ability to switch to air mode to overcome obstacles. The design features, control algorithms, and energy consumption optimization are analyzed. Potential areas of application include rescue operations, military reconnaissance, and exploration of hard-to-reach areas.

IBR-SLAM: Visual SLAM Based on Improved BiSeNet with RGB-D Sensor

Abstract Visual Simultaneous Localization and Mapping (VSLAM) is the key technology of mobile robots’ localization and mapping. At present, the VSLAM system has high robustness in static environments, but it will cause feature point mapping errors in dynamic environments, which will affect the robustness of the system. To improve this situation, this study proposes a dynamic robust SLAM framework IBR-SLAM. This framework combines enhanced semantic segmentation and multimodal geometric constraints. The system acquired images by RGB-D camera, extracted semantic information of images through improved BiSeNet and used this information combined with the geometric constraints in the adaptive model to determine the dynamic region. In the dense mapping thread, the point cloud in the dynamic region is removed, so as to construct an accurate static global point cloud map. At last, the proposed system is tested on two datasets, TUM and Bonn, and compared with ORB-SLAM3, the absolute trajectory error is improved by 97.33% and 89.79% respectively. The results show that IBR-SLAM maintains high robustness in various dynamic scenarios.

Prescribed-Time Tracking Over Total-Time-Domain for Nonlinear Systems Subject to Mismatched Disturbance: An ESO-Based Control Strategy.

This article aims to investigate the performance-guaranteed tracking problem for a class of uncertain nonlinear systems. The main goal is to attain control performance within prescribed-time (PT) limits despite the existence of mismatched disturbances. First, the mismatched disturbances are transformed into the equivalent forms. Second, for the unknown disturbance estimation, a PT extended state observer (PTESO) is developed to switch between the prescribed settling time p, the order is diminished to alleviate the occurrence of the peaking phenomenon. Furthermore, an ESO-based PT control strategy is constructed with time-varying gains. This allows real-time compensation of disturbances, and the prescribed performance is attained by virtue of a Lyapunov function employed combines both barrier and quadratic forms. Ultimately, the benefits and efficacy are illustrated via a numerical demonstration involving a wheeled mobile robot. The key features of this article include the observer capability to estimate unknown mismatching disturbances and the full effectiveness of the proposed controller for t∈[t0,∞), ensuring the convergence of tracking error to zero within any PT. Consequently, in addition to achieving the output tracking objective, the system can also exhibit favorable transient performance.

Multi-Sensor Fusion for Autonomous Mobile Robot Docking: Integrating LiDAR, YOLO-Based AprilTag Detection, and Depth-Aided Localization

Reliable and accurate docking remains a fundamental challenge for autonomous mobile robots (AMRs) operating in complex industrial environments with dynamic lighting, motion blur, and occlusion. This study proposes a novel multi-sensor fusion-based docking framework that significantly enhances robustness and precision by integrating YOLOv8-based AprilTag detection, depth-aided 3D localization, and LiDAR-based orientation correction. A key contribution of this work is the construction of a custom AprilTag dataset featuring real-world visual disturbances, enabling the YOLOv8 model to achieve high-accuracy detection and ID classification under challenging conditions. To ensure precise spatial localization, 2D visual tag coordinates are fused with depth data to compute 3D positions in the robot’s frame. A LiDAR group-symmetry mechanism estimates heading deviation, which is combined with visual feedback in a hybrid PID controller to correct angular errors. A finite-state machine governs the docking sequence, including detection, approach, yaw alignment, and final engagement. Simulation and experimental results demonstrate that the proposed system achieves higher docking success rates and improved pose accuracy under various challenging conditions compared to traditional vision- or LiDAR-only approaches.

An AR Programming Approach to Incorporate Spatial Multimodal Interaction

Abstract With the wide application of mobile robots, traditional robot programming faces a series of challenges due to its complex task settings and user interactions. In the context of Industry 5.0, we are committed to researching a spatial programming s method based on AR (Augmented Reality ) technology to realize human-robot collaboration. In this paper, the AR-based spatial perception method is studied, and the virtual coordinate system in AR is established through the virtual-reality model fusion perception method. By analyzing the relationship between each virtual coordinate system, the robot virtual space operation is planned, and then the physical world robot operation is controlled. A spatial multimodal AR interaction interface covering gesture, speech, EEG signals and gaze control is designed to realize different functions for each modal interaction method. In the laboratory environment, the feasibility and effectiveness of the system are tested and verified by taking the coal mining machine interceptor tooth repair task as an example, and selecting multiple coal mining machine interceptor tooth repairs for flexible operation tasks in the testing process.The results of the study show that the spatial programming method based on AR technology can significantly improve the efficiency of task execution of operator-controlled mobile robots and enhance the human-robot interaction experience.

Implementation of Odometry with EKF in Hector SLAM Methods

Map building for plain spatial soundings, such as a long and straight corridor in simultaneous localization and mapping (SLAM) is a challenging problem because of lacks of distinguishable landmarks. Such an environment is highly possible to induce erroneous mapping results, such as alias problems. This paper presents a scan matching algorithm with odometer prediction using Extended Kalman Filter (EKF) and an optimal path planning based on regression subgoals. The scan matching process can relax the problems of local minima by means of an effective correction in the odometrical information. By iterating odometrical corrections in each step of running motion model, the matching result can be better than one only believes in individual information from scanning or odometry. Meanwhile, an optimal path planning utilizing an A* algorithm with a regression method is introduced to ensure a mobile robot be able to move elaborately around the corner and speed up along a straight line. Experiments in an indoor environment have been conducted to verify the effectiveness and validation of the proposed techniques.

Performance Improvement of Pure Pursuit Algorithm via Online Slip Estimation for Off-Road Tracked Vehicle

The motion control of a tracked mobile robot remains an important capability for autonomous navigation. Kinematic path-tracking algorithms are commonly used in mobile robotics due to their ease of implementation and real-time computational cost advantage. This paper integrates an extended Kalman filter (EKF) into a common kinematic controller for path-tracking performance improvement. The extended Kalman filter estimates the instantaneous center of rotation (ICR) of tracks using the sensor readings of GPS and IMU. These ICR estimations are then given as input to the motion control algorithm to generate the track velocity demands. The platform to be controlled is a heavyweight off-road tracked vehicle, which necessitates the investigation of slip values. A high-fidelity simulation model, which is verified with field tests, is used as the plant in the path-tracking simulations. The performance of the filter and the algorithm is also demonstrated in field tests on a stabilized road. The field results show that the proposed estimation increases the path-tracking accuracy significantly (about 44%) compared to the classical pure pursuit.

Deploying an Educational Mobile Robot

This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped with odometry and inertial measurement units (IMUs), to gather comprehensive motion data. To enhance the reliability and interpretability of the data, advanced data processing techniques—such as moving averages, correlation analysis, and exponential smoothing—were employed. Python-based tools, including Matplotlib and Visual Studio Code, were used for data visualization and analysis. The analysis provided key insights into the robot’s motion dynamics; specifically, its stability during linear movements and variability during turns. By applying moving average filtering and exponential smoothing, noise in the sensor data was significantly reduced, enabling clearer identification of motion patterns. Correlation analysis revealed meaningful relationships between velocity and acceleration during various motion states. These findings underscore the value of advanced data processing techniques in improving the performance and reliability of educational mobile robots. The insights gained in this pilot project contribute to the optimization of navigation algorithms and motion control systems, enhancing the robot’s future potential in STEM education applications.

Smart Terrain-Aware Navigation: An Embedded Robotic System for Obstacle Avoidance and Surface Detection

Abstract: This paper presents the creation of an economical, self-sufficient mobile robot intended for real-time obstacle avoidance and detection of uneven surfaces, ensuring safe and efficient navigation in unstructured settings. Constructed on the Arduino Uno platform, the system incorporates an HC-SR04 ultrasonic sensor for proximity-based obstacle detection and an MPU6050 accelerometer/gyroscope module to identify surface inclinations and irregular terrains. Additionally, the robot features an L298N motor driver that facilitates precise movement control, while a 16×2 LCD module offers ongoing feedback regarding system status and environmental conditions. The approach includes sensor fusion, modular hardware integration, and embedded software design, enabling robust decision-making and real-time adaptability. Experimental assessments reveal the system’s capability to navigate various terrains and avoid obstacles with minimal latency and high precision. The design's modularity, cost-effectiveness, and ease of deployment render it suitable for numerous applications, such as industrial automation, educational robotics, exploration, and disaster response. The findings highlight the potential of integrating obstacle avoidance with surface detection within a cohesive framework to improve autonomous robotic mobility in intricate real-world situations.

Type-2 Fuzzy-Controlled Air-Cleaning Mobile Robot

This research presents the development of a type-2 fuzzy-controlled autonomous mobile robot specifically designed for monitoring and actively maintaining indoor air quality. The core of this system is the proposed type-2 fuzzy PID dual-mode controller used for stably patrolling rooms along the walls of the environment. The design method ingeniously merges the fast error correction capability of PID control with the robust adaptability of type-2 fuzzy logic control, which utilizes interval type-2 fuzzy sets. Furthermore, the type-2 fuzzy rule table of the right wall-following controller can be extended from the first designed fuzzy left wall-following controller in a symmetrical design manner. As a result, this study eliminates the drawbacks of excessive oscillations arising from PID control and sluggish response to large initial errors in typical traditional fuzzy control. The following of the stable wall and obstacle is facilitated with ensured accuracy and easy implementation so that effective air quality monitoring and active PM2.5 filtering are achieved in a movable manner. Furthermore, the augmented reality (AR) interface overlays real-time PM2.5 data directly onto a user’s visual field, enhancing situational awareness and enabling an immediate and intuitive assessment of air quality. As this type of control is different from that used in traditional fixed sensor networks, both broader area coverage and efficient air filtering are achieved. Finally, the experimental results demonstrate the controller’s superior performance and its potential to significantly improve indoor air quality.

Compliant Force Control for Robots: A Survey

Compliant force control is a fundamental capability for enabling robots to interact safely and effectively with dynamic and uncertain environments. This paper presents a comprehensive survey of compliant force control strategies, intending to enhance safety, adaptability, and precision in applications such as physical human–robot interaction, robotic manipulation, and collaborative tasks. The review begins with a classification of compliant control methods into passive and active approaches, followed by a detailed examination of direct force control techniques—including hybrid and parallel force/position control—and indirect methods such as impedance and admittance control. Special emphasis is placed on advanced compliant control strategies applied to structurally complex robotic systems, including aerial, mobile, cable-driven, and bionic robots. In addition, intelligent compliant control approaches are systematically analyzed, encompassing neural networks, fuzzy logic, sliding mode control, and reinforcement learning. Sensorless compliance techniques are also discussed, along with emerging trends in hardware design and intelligent control methodologies. This survey provides a holistic view of the current landscape, identifies key technical challenges, and outlines future research directions for achieving more robust, intelligent, and adaptive compliant force control in robotic systems.

Robotics redefining wheat farming: Bridging efficiency and sustainability

Automation in wheat cultivation has revolutionized precision, sustainability and overall productivity by integrating advanced robotics and cutting-edge technology. High-clearance robots automate all growth stages by employing adaptive Kalman filters and fuzzy PID controllers for precisely controlled navigation and acquisition of phenotypic data. Swarm robots are cost-effective and exhibit adaptability to varied field conditions challenging traditional economies of scale, enabling smaller farms to achieve competitive production costs. Advances in image processing have overcome the challenges of canopy closure, enabling sub-50 mm accuracy in wheat row tracking, critical for early-growth interventions. Integration of LiDAR, spectral sensors and aerial robotics complements ground-based systems, offering robust data for decision support. Deployment of mobile robots has enhanced precision seeding with accuracy reaching over 93 % while high-throughput phenotyping platforms utilize robotics and machine learning to transform disease resistance assessments, such as Fusarium Head Blight (FHB). Algorithms like DeepLabV3+ have achieved over 96 % accuracy in identifying wheat ears, significantly reducing labour in breeding resistant varieties. The seed screener platform automates the analysis of single wheat kernels, combining RGB imaging and near-infrared (NIR) spectroscopy to evaluate 3D morphological and biochemical traits. The seed screener uses the marching cubes algorithm to extract precise morphological data from 3D visual models. This high-precision, high-throughput platform demonstrates significant potential for commercialisation, providing breeders with an advanced tool to facilitate wheat improvement programmes. These innovations address critical challenges, including phenotypic characterisation, planting uniformity and real-time adaptability, offering transformative solutions for precision agriculture. Automation in wheat cultivation provides a pathway to achieving food security while ensuring sustainability, ushering in a new era in agricultural practices.