Search Results (65)

The increasing inclusion of robots in social areas requires continuous improvement of behavioral strategies that robots must follow. Although behavioral strategies mainly focus on operational efficiency, other aspects should be considered to provide a reliable interaction in terms of sociability (e.g., methods for detection and interpretation of human behaviors, how and where human–robot interaction is performed, and participant evaluation of robot behavior). This scoping review aims to answer seven research questions related to robotic motion in socially aware navigation, considering some aspects such as: type of robots used, characteristics, and type of sensors used to detect human behavioral cues, type of environment, and situations. Articles were collected on the ACM Digital Library, Emerald Insight, IEEE Xplore, ScienceDirect, MDPI, and SpringerLink databases. The PRISMA-ScR protocol was used to conduct the searches. Selected articles met the following inclusion criteria. They: (1) were published between January 2018 and August 2025, (2) were written in English, (3) were published in journals or conference proceedings, (4) focused on social robots, (5) addressed Socially Aware Navigation (SAN), and (6) involved the participation of volunteers in experiments. As a result, 22 studies were included; 77.27% of them employed mobile wheeled robots. Platforms using differential and omnidirectional drive systems were each used in 36.36% of the articles. 50% of the studies used a functional robot appearance, in contrast to bio-inspired appearances used in 31.80% of the cases. Among the frequency of sensors used to collect data from participants, vision-based technologies were the most used (with monocular cameras and 3D-vision systems each reported in 7 articles). Processing was mainly performed on board (50%) of the robot. A total of 59.1% of the studies were performed in real-world environments rather than simulations (36.36%), and a few studies were performed in hybrid environments (4.54%). Robot interactive behaviors were identified in different experiments: physical behaviors were present in all experiments. A few studies employed visual behaviors (2 times). In just over half of the studies (13 studies), participants were asked to provide post-experiment feedback. Full article

Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular ($\omega$) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for $\omega$, with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations. 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

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations—pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks. Yet, its performance relies on manually tuned parameters and lacks adaptability to dynamic changes. This study introduces a hybrid grey wolf-particle swarm optimization (GPSO) algorithm, combining the exploratory ability of a grey wolf optimizer with the rapid convergence of particle swarm optimization. The GPSO algorithm adaptively tunes MPC parameters online to improve control. A kinematic model of a four-wheeled differential-drive robot is developed, and an MPC controller using error-state linearization is implemented. GPSO integrates hierarchical leadership and chaotic disturbance strategies to enhance global search and local convergence. Simulation experiments on circular and double-lane-change trajectories show that GPSO-MPC outperforms standard MPC and PSO-MPC in tracking accuracy, heading stability, and control smoothness. The results confirm the improved adaptability and robustness of the proposed method, supporting its effectiveness in dynamic underwater environments. Full article

With the application of tracked mobile robots in detection and rescue, how to improve their stability and trafficability has become the research focus. In order to improve the driving ability and trafficability of tracked mobile robots in rugged terrain, this paper proposes a new type of tracked mobile robot using passive suspension. By adding a connecting rod differential mechanism between the left and right track mechanisms, the contact stability between the track and terrain is enhanced. The kinematics model and attitude relationship of the suspension are analyzed and established, and the rationality of the passive suspension scheme is verified by dynamic simulation. The simulation results show that the tracked robot with passive suspension shows good obstacle surmounting performance, but there will be a heading deflection problem. Therefore, a track drive speed of the driving state compensation control is proposed based on the driving scene, which can effectively solve the problem of slip and heading deflection. Through the field test of the robot prototype, the effectiveness of the suspension scheme and control system is verified, which provides a useful reference for the scheme design and performance improvement of the tracked mobile robot in complex field scenes. Full article

This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system’s effectiveness and suitability for real-time mobile robot navigation in indoor environments. Full article

Navigation is a fundamental technology for mobile robots. However, many trajectory planning methods suffer from curvature discontinuities, leading to instability during robot operation. To address this challenge, this paper proposes a navigation scheme that adheres to the kinematic constraints of a two-wheeled differential-drive robot. An improved and efficient RRT algorithm is employed for global navigation, while an adaptive clothoid curve is utilized for local trajectory smoothing. Simulation results demonstrate that the proposed method effectively eliminates curvature discontinuities and enhances operational efficiency. Full article

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming to enhance adaptability and multi-objective optimization. Initially, GPs are utilized to model the uncertain dynamics of agents based on sensor data, providing a stable and noiseless training virtual environment for the first phase of TD3 strategy network training. Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. Additionally, the approach employs an enhanced dual-layer reward mechanism tailored to different stages of learning, ensuring robustness and improved convergence speed. Experimental results using a differential drive robot simulation demonstrate the superiority of this method over traditional controllers. The integration of the TD3 compensation network further improves the cooperative reward among agents. Full article

In the evolving field of mobile robotics, enhancing localization robustness in dynamic environments remains a critical challenge, particularly for ROS 2-based systems where sensor fusion plays a pivotal role. This study evaluates an AI-driven approach to dynamically adjust covariance parameters for improved pose estimation in a differential-drive mobile robot. A regression model was integrated into the robot_localization package to adapt the Extended Kalman Filter (EKF) covariance in real time, with experiments conducted in a controlled indoor setting over runs comparing AI-enabled dynamic covariance prediction against a static covariance baseline across Static, Moderate, and Aggressive motion dynamics. The AI-enabled system achieved a Mean Absolute Error (MAE) of 0.0061 for pose estimation and reduced median yaw prediction errors to 0.0362 rad (static) and 0.0381 rad (moderate) with tighter interquartile ranges (0.0489 rad, 0.1069 rad) compared to the baseline (0.0222 rad, 0.1399 rad). Aggressive dynamics posed challenges, with errors up to 0.9491 rad due to data distribution bias and Random Forest model constraints. Enhanced dataset augmentation, LSTM modeling, and online learning are proposed to address these limitations. Datalogging enabled iterative re-training, supporting scalable state estimation with future focus on online learning. Full article

Researchers are actively exploring advanced algorithms to enhance robots’ ability to navigate complex environments while avoiding obstacles. Four different environments were designed in the Webots simulator, including a mobile robot, a goal, a static obstacle, and one or two dynamic obstacles. The robot’s state vector was determined based on its position, the goal, and sensor variables, with all elements randomly placed in each learning and test step. A multi-layer perceptron (MLP) agent was trained for 1000 episodes in these environments using classical and fuzzy logic-based reward functions. After the training process was completed, the agents trained with the fuzzy logic-based reward function were tested for each environment. As a result of the test, while the robot’s arrival rate was 100% in the first three environments, it was measured as 91% in the fourth environment. In the last environment, the rate of crashing into a wall or dynamic obstacle was observed to be 7%. In addition, the agent trained in the fourth environment was found to successfully reach the target in multi-robot environments. The agent trained fuzzy logic-based reward function obtained the best result for four different environments. Based on these results, a fuzzy logic-based reward function was proposed to address the tuning problem of the classical reward function. It was demonstrated that a robust fuzzy logic-based reward function was successfully designed. This study contributed to the literature by presenting a reinforcement learning-based safe navigation algorithm incorporating a fuzzy logic-based reward function. Full article

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

This paper is devoted to the solution of the optimal control problem. The obtained control should be optimal in terms of quality criteria and, at the same time, feasible when implemented in the control object. To solve the optimal control problem in the class of feasible control functions, an advanced mathematical model of the control object is used. Firstly, the universal stabilisation system of the motion along any trajectory from some class is developed via symbolic regression. Then, the obtained stabilisation system is inserted into the right part of the control object model instead of the control vector. A reference model with a free control vector in the right part is added to the model; thus, the advanced mathematical model of the control object is obtained. After this, the optimal control problem is solved with the advanced mathematical model of the control object. The optimal control problem is stated in the classical form when the control is a time function. Here, the control function is searched for the reference model. The preliminary design of the universal stabilisation system for some class of trajectories allows the solution of the optimal control problem via the control object in a reasonable time frame. The proposed methodology is computationally tested for a model of the spatial motion of a quadcopter and a group of two-wheeled mobile robots with a differential drive. The results of the experiments show that the universal stabilisation system ensures the stabilisation of the motion of the objects along optimal trajectories, which are not known beforehand but obtained as a result of solving the problem with an advanced model. Full article

The paper presents the design of iterative learning control for a class of mobile robots. This control strategy allows driving the considered system, which executes the same control task in trials, to the predefined reference within the consecutive iterations by improving the control signal gradually. The control problem being stated concerns a mobile robot, and hence, its kinematic model is presented. The considered model is nonlinear as it is related to the robot orientation angle. Thus, the linearization strategy is introduced by dividing the range of possible orientation angles to four quarters and then deriving a linear parameter-varying system. As a distinct research topic, the feasible/optimal number selection of polytope vertices of each LPV submodel are considered. Next, for the resulting bank of models, the switched iterative control scheme is transformed into closed-loop differential linear repetitive processes. Subsequently, based on the fact that ensuring the so-called stability along the trial is equivalent to the convergence of the original model output to the predefined reference, an appropriate stabilization condition is applied in order to compute the feedback controller gains. The overall effectiveness and performance of the proposed methodology are evaluated through comprehensive simulation examples. Full article

In this paper, a consensus-based formation control strategy is presented, subject to area constraints and collision avoidance. To achieve a desired formation pattern, a control law is proposed that incorporates a vertex-tension function along with signed area constraints. The vertex-tension function provides the capabilities of collision avoidance among agents. Moreover, signed area constraints avoid local minimum stagnation and mitigate ambiguities within the formation shape. Additionally, the proposed approach can be implemented considering a group of differential-drive mobile robots in both centralized and decentralized settings. Simulation and real-world experiments are performed to validate the effectiveness of the proposed approach, where the experimental setup includes a multi-robot system composed of Turtlebot3^® Waffle Pi robots. Full article

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications. Full article