Search Results (18)

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics. Full article

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

A decentralized artificial intelligence (DAI) system is a human-oriented artificial intelligence (AI) system, which performs self-learning and shares its knowledge with other DAI systems like humans. A DAI device is an individual device (e.g., a mobile phone, a personal computer, a robot, a car, etc.) running a DAI system. A DAI device acquires validated knowledge data and raw data from a blockchain system as a trust anchor and improves its knowledge level by self-learning using the validated data. A DAI device using the proposed system reduces unreliable tasks, including the generation of unreliable products (e.g., deepfakes, fake news, and hallucinations), but the proposed system also prevents these malicious DAI devices from acquiring the validated data. This paper proposes a new architecture for a blockchain-based data management system for DAI devices, together with the service scenario and data flow, security threats, and security requirements. It also describes the key features and expected effects of the proposed system. This paper discusses the considerations for developing or operating the proposed system and concludes with future works. Full article

We describe a quantum teleportation protocol for exchanging data between a mobile robot and its control station. Because of the high cost of quantum network systems, we use MATLAB software to simulate the teleportation of data. Our simulation models the dynamic motion of a car-like mobile robot (CLMR), considering its mass and inertia and the environmental viscosity. Our remote control method accurately reproduces a mathematical model of the CLMR’s real-world motion. The CLMR’s trajectory is represented by differential equations, with the velocity calculated using the Jacobian matrix. The velocity inputs are teleported from the control station to the CLMR, enabling it to move. Nevertheless, physical constraints cause the deviation of the robot’s trajectory from the predicted trajectory. To correct this deviation, the CLMR’s current position is teleported to the control station. Before implementing this protocol, we calculate the quantum teleportation circuit, and we use quantum gates in matrix form to simulate the data teleportation process. The protocol’s accuracy is assessed by comparing the original data and teleported data, and a good match is obtained. This study demonstrates the feasibility of quantum teleportation for remotely controlling real-time robotic systems over long distances and in environments that interfere with classical wireless communication. Full article

The goal of this research is to formulate and compare two algorithms, classical particle swarm optimisation (PSO) and quantum PSO (QPSO), for optimising the motion of a car-like mobile robot. Both algorithms are evaluated on the basis of their reduction and stabilisation of the root mean square error (RMSE) between the robot’s desired and actual trajectories. An implementation of the robot’s dynamic motion is provided. The robot’s mass and inertia are considered. The robot’s settings and the viscosity of the surroundings present a few obstacles to following the specified path. For each algorithm, the proportional ($K_p$) and derivative ($K_d$) parameters of the controller are optimised, and the convergence speeds and stabilities of the controllers are compared. The results show that both algorithms perform comparably. However, the QPSO method converges faster and is more stable at optimal $K_p$ and $K_d$ values. The ramifications of this research extend beyond trajectory tracking. Enhanced optimisation approaches can lead to higher performance in a variety of robotic systems, including autonomous cars, drones, and automation systems, by employing advanced quantum algorithms, such as QPSO. Full article

TEB (timed elastic band) can efficiently generate optimal trajectories that match the motion characteristics of car-like robots. However, the quality of the generated trajectories is often unstable, and they sometimes violate boundary conditions. Therefore, this paper proposes a fuzzy logic control–TEB algorithm (FLC-TEB). This method adds smoothness and jerk objectives to make the trajectory generated by TEB smoother and the control more stable. Building on this, a fuzzy controller is proposed based on the kinematic constraints of car-like robots. It uses the narrowness and turning complexity of the trajectory as inputs to dynamically adjust the weights of TEB’s internal objectives to obtain stable and high-quality trajectories in different environments. The results of real car-like robot tests show that compared to the classical TEB, FLC-TEB increased the trajectory time by 16% but reduced the trajectory length by 16%. The trajectory smoothness was significantly improved, the change in the turning angle on the trajectory was reduced by 39%, the smoothness of the linear velocity increased by 71%, and the smoothness of the angular velocity increased by 38%, with no reverse movement occurring. This indicates that when planning trajectories for car-like mobile robots, while FLC-TEB slightly increases the total trajectory time, it provides more stable, smoother, and shorter trajectories compared to the classical TEB. Full article

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system. Full article

In this paper, a trajectory tracking control strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and external disturbances. First, modeling errors and external disturbances are introduced into an ideal kinematic model of a CLMR, and a set of output equations is utilized to split the coupled, underdriven disturbance kinematic model into two mutually independent subsystems. Next, disturbances in the subsystems are estimated based on a linear ESO, and the convergence of the proposed observer is proved by the Lyapunov method. Finally, a controller with disturbance compensation is designed using backstepping control to complete the trajectory tracking task of CLMRs. Simulation and experimental results show the effectiveness of the proposed control scheme. Full article

Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method’s effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios. Full article

This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding. Full article

This paper presents a nonlinear, universal, path-following controller for Wheeled Mobile Robots (WMRs). This approach, unlike previous algorithms, solves the path-following problem for all common categories of holonomic and nonholonomic WMRs, such as omnidirectional, unicycle, car-like, and all steerable wheels. This generality is the consequence of a two-stage solution that tackles separately the platform path-following and wheels’ kinematic constraints. In the first stage, for a mobile platform divested of the wheels’ constraints, we develop a general paradigm of a path-following controller that plans asymptotic paths from the WMR to the desired path and, accordingly, we derive a realization of the presented paradigm. The second stage accounts for the kinematic constraints imposed by the wheels. In this stage, we demonstrate that the designed controller simplifies the otherwise impenetrable wheels’ kinematic and nonholonomic constraints into explicit proportional functions between the velocity of the platform and that of the wheels. This result enables us to derive a closed-form trajectory generation scheme for the asymptotic path that constantly keeps the wheels’ steering and driving velocities within their corresponding, pre-specified bounds. Extensive experimental results on several types of WMRs, along with simulation results for the other types, are provided to demonstrate the performance and the efficacy of the method. Full article

In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments. Full article

Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters. Full article

The construction industry is currently technologically challenged to incorporate new developments for enhancing the process, such as the use of 3D printing for complex building structures, which is the aim of this brief. To do so, we show a systematic study regarding the usability and performance of mobile manipulators as displaceable 3D printing machinery in construction sites, with emphasis on the three main different existing mobile platforms: the car-like, the unicycle and the omnidirectional (mecanum wheeled), with an UR5 manipulator on them. To evaluate its performance, we propose the printing of the following building elements: helical, square, circular and mesh, with different sizes. As metrics, we consider the total control effort observed in the robots and the total tracking error associated with the energy consumed in the activity to get a more sustainable process. In addition, to further test our work, we constrained the robot workspace thus resembling real life construction sites. In general, the statistical results show that the omnidirectional platform presents the best results –lowest tracking error and lowest control effort– for circular, helicoidal and mesh building elements; and car-like platform shows the best results for square-like building element. Then, an innovative performance analysis is achieved for the printing of building elements, with a contribution to the reduction of energy consumption. Full article

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization. Full article