Search Results (35)

This paper addresses global path planning for a wheeled mobile robot with two different kinematic structures, considering both shortest path and minimum energy consumption criteria. The main research question concerns how the robot’s kinematic structure and the selected planning algorithm influence the resulting path with respect to these criteria. Our review of the state of the art discusses selected path planning methods, including model-based approaches. To determine the energy optimal path, a simplified model of the PIAP GRANITE robot was developed. The robot can be configured as either differentially driven or skid-steered. In the differentially driven configuration, the robot has two driven wheels and two caster wheels, whereas in the skid-steered configuration all wheels are independently driven. The robot’s models are based on previous theoretical and experimental studies and include kinematics, dynamics, drive units, and wheel slip phenomena. For path planning, it was assumed that the robot can move straight or turn. A flat terrain representative of typical urban environments was modeled as a grid of square cells, each characterized by friction and rolling resistance coefficients. Path planning was performed using A*, Theta*, and RRT* algorithms. In order to quantitatively evaluate the results, quality indexes were defined, including path length, energy consumption, computation time, and the number of analyzed nodes. Simulation results are presented for selected terrain maps, both robot configurations, all algorithms, and both optimization criteria. The results show that the differentially driven configuration is consistently more energy-efficient. For the skid-steered robot, minimizing the number of turns is crucial due to high turning energy costs. The A* algorithm consistently finds optimal paths, whereas RRT* is faster but produces non-optimal and non-repeatable results. Theta* does not always achieve optimality due to limitations imposed by the line-of-sight function. Full article

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of mobile robotics involves coordinated groups of autonomous robots, commonly referred to as swarms. However, only a limited number of studies have addressed systems in which ropes or wires physically connect robots. Connecting multiple autonomous robotic vehicles with a tensioned wire can form a movable fence, enabling coordinated motion as a single dynamic entity. This paper presents a real-time control approach for the off-road motion of physically connected skid-steering robotic vehicles. A numerical-simulation-driven artificial neural network is employed as a surrogate model to estimate wheel–ground load distribution online, enabling stable steering control and accurate trajectory tracking on rough terrain while accounting for wire-induced coupling effects. Full article

This study introduces an efficient real-time lane detection and navigation system for greenhouse environments, leveraging the LaneATT architecture. Designed for deployment on the Jetson Xavier NX edge computing platform, the system utilizes an RGB camera to enable autonomous navigation in greenhouse rows. From real-world agricultural environments, data were collected and annotated to train the model, achieving 90% accuracy, 91% F1 Score, and an inference speed of 48 ms per frame. The LaneATT-based vision system was trained and validated in greenhouse environments under heterogeneous illumination conditions and across multiple phenological stages of crop development. The navigation system was validated using a commercial skid-steering mobile robot operating within an experimental greenhouse environment under actual operating conditions. The proposed solution minimizes computational overhead, making it highly suitable for deployment on edge devices within resource-constrained environments. Furthermore, experimental results demonstrate robust performance, with precise lane detection and rapid response times on embedded systems. Full article

Compact heavy-duty skid-steer robots are increasingly used for city logistics and intralogistics tasks where high payload capacity and stability are required. However, their limited maneuverability and non-negligible turning radius challenge conventional waypoint-tracking controllers that assume unconstrained motion. This paper proposes a curvature-constrained trajectory planning and control framework that guarantees geometrically feasible motion for such platforms. The controller integrates an explicit curvature limit into a finite-state machine, ensuring smooth heading transitions without in-place rotation. The overall architecture integrates GNSS-RTK and IMU localization, modular ROS 2 nodes for trajectory execution, and a supervisory interface developed in Foxglove Studio for intuitive mission planning. Field trials on a custom four-wheel-drive skid-steer platform demonstrate centimeter-scale waypoint accuracy on straight and curved trajectories, with stable curvature compliance across all tested scenarios. The proposed method achieves the smoothness required by most applications while maintaining the computational simplicity of geometric followers. Computational simplicity is reflected in the absence of online optimization or trajectory reparameterization; the controller executes a constant-time geometric update per cycle, independent of waypoint count. The results confirm that curvature-aware control enables reliable navigation of compact heavy-duty robots in semi-structured outdoor environments and provides a practical foundation for future extensions. Full article

This paper presents a mobile robotic system designed for autonomous navigation and forest and tree trait estimation, with a focus on the location of individual trees and the diameter of the trunks. The system integrates light detection and ranging data and images using a framework based on simultaneous localization and mapping (SLAM) and a deep learning model for trunk segmentation and tree keypoint detection. Field experiments conducted in a wooded area in Udine, Italy, using a skid-steered mobile robot, demonstrate the effectiveness of the system in navigating, while avoiding obstacles (even in cases where the Global Navigation Satellite System signal is not reliable). The results highlight that the proposed robotic system is capable of autonomously generating maps of forests as point clouds with minimal drift thanks to the loop closure strategy integrated in the SLAM algorithm, and estimating tree traits automatically. Full article

Bilateral teleoperation of skid-steering mobile robots with time-varying delays presents significant challenges in ensuring accurate leader–follower coupling. This article presents a novel controller for a bilateral teleoperation system composed of a robot manipulator and a skid-steering mobile robot. The proposed controller leverages neural networks to compensate for ground–robot interactions, uncertain dynamics, and communication delays. The control strategy integrates a shared scheme between damping injection and two neural networks, enhancing the robustness and adaptability of the delayed system. A rigorous theoretical analysis of the closed-loop teleoperation system is provided, establishing conditions of control parameters to ensure stability and convergence of the coordination errors. The proposed method is validated through numerical testing, demonstrating strong agreement between theoretical outcomes and simulation results. Full article

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments. Full article

To enhance the precision and real-time performance of trajectory tracking control in differential-steering intelligent sweeping robots and to improve the adaptability of the control algorithm to errors caused by sensor noise, tire slip, and skid, an MPC-PID (Model Predictive Control–Proportional-Integral-Derivative) dual closed-loop control strategy was proposed. This strategy integrates a Kalman filter-based state estimator and a sliding compensation module. Based on the kinematic model of the intelligent sweeping robot, a model predictive controller (MPC) was designed to regulate the vehicle’s pose, while a PID controller was used to adjust the longitudinal speed, forming a dual closed-loop control algorithm. A Kalman filter was employed for state estimation, and a sliding compensation module was introduced to mitigate wheel slip and lateral drift, thereby improving the stability of the control system. Simulation results demonstrated that, compared to traditional MPC control, the maximum lateral deviation, maximum heading angle deviation, and speed response time were reduced by 50.83%, 53.65%, and 7.10%, respectively, during sweeping operations. In normal driving conditions, these parameters were improved by 41.58%, 45.54%, and 24.17%, respectively. Experimental validation on an intelligent sweeper platform demonstrates that the proposed algorithm achieves a 16.48% reduction in maximum lateral deviation and 9.52% faster speed response time compared to traditional MPC, effectively validating its enhanced tracking effectiveness in intelligent cleaning operations. Full article

This article focuses on trajectory tracking control of a four-wheeled mobile robot, with non-steered wheels. The issues in terms of robot kinematics are discussed and a dynamics model is derived, which additionally took into account the drive unit model. This paper analyses four versions of the control system, which take into account the possibility of compensating for wheel slip and non-linearities resulting from the drive unit model. It is assumed that the wheel-slip compensation is based on the measurement of the actual robot’s motion parameters. The linear and angular motion parameters of the robot’s mobile platform are taken into account, which allows for the estimation of the wheel slip velocities. The results of the simulation studies are presented, consisting of the evaluation of individual control system solutions in terms of achieving the highest possible accuracy in executing a prescribed trajectory. Additionally, the impact of the investigated control strategies on electric power demand and electric energy consumption by the robot’s drives is analyzed. In order to quantitatively assess the control system solutions, quality indexes were adopted, focusing on tracking accuracy and energy efficiency. The research results indicate that incorporating wheel-slip compensation into the control system enables high accuracy to be achieved in terms of trajectory tracking. In turn, the use of the drive unit model within the control system leads to an increase in the accuracy of the robot’s wheel movements, which does not ultimately result in an increase in the accuracy of the motion of the robot’s mobile platform due to the slipping of the wheels. It was also observed that improving the trajectory tracking accuracy leads to an increase in the maximum electric power demand and electric energy consumption by the robot’s drives. Full article

Skid-steer mobile robots (SSMRs) are ubiquitous in indoor and outdoor applications. Their accurate trajectory tracking control is quite challenging due to the uncertainties arising from the complex behavior of frictional force, external disturbances, and fluctuations in the instantaneous center of rotation (ICR) during turning maneuvers. These uncertainties directly disturb velocities, hindering the robot from tracking the velocity command. This paper proposes a nonsingular terminal sliding mode control (NTSMC) based on backstepping for a four-wheel SSMR to cope with the aforementioned challenges. The strategy seeks to mitigate the impacts of external disturbances and model uncertainties by developing an adaptive law to estimate the integrated lumped outcome. The finite time stability of the closed-loop system is proven using Lyapunov’s theory. The designed NTSMC input is continuous and avoids noticeable chattering. It was noted in the simulation analysis that the proposed control strategy is strongly robust against disturbance and modeling uncertainties, demonstrating effective trajectory tracking performance in the presence of disturbance and modeling uncertainties. Full article

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions. Full article

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (${\mathrm{IRRT}}^{*}$) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the ${\mathrm{IRRT}}^{*}$ planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the ${\mathrm{IRRT}}^{*}$ algorithm outperformed RRT and ${\mathrm{RRT}}^{*}$ with 96.1% and 97.6% smoother paths, respectively. The ${\mathrm{IRRT}}^{*}$ also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture. Full article

In response to the challenges of multiple personnel, heavy support tasks, and high labor intensity in coal mine tunnel drilling and anchoring operations, this study proposes a novel tracked drilling and anchoring robot. The robot is required to maintain alignment with the centerline of the tunnel during operation. However, owing to the effects of skidding and slipping between the track mechanism and the floor, the precise control of a drilling and anchoring robot in tunnel environments is difficult to achieve. Through an analysis of the body and track mechanisms of the drilling and anchoring robot, a kinematic model reflecting the pose, steering radius, steering curvature, and angular velocity of the drive wheel of the drilling and anchoring robot was established. This facilitated the determination of speed control requirements for the track mechanism under varying driving conditions. Mathematical models were developed to describe the relationships between a tracked drilling and anchoring robot and several key factors in tunnel environments, including the minimum steering space required by the robot, the minimum relative steering radius, the steering angle, and the lateral distance to the sidewalls. Based on these models, deviation-correction control strategies were formulated for the robot, and deviation-correction path planning was completed. In addition, a PID motion controller was developed for the robot, and trajectory-tracking control simulation experiments were conducted. The experimental results indicate that the tracked drilling and anchoring robot achieves precise control of trajectory tracking, with a tracking error of less than 0.004 m in the x-direction from the tunnel centerline and less than 0.001 m in the y-direction. Considering the influence of skidding, the deviation correction control performance test experiments of the tracked drilling and anchoring robot at dy = 0.5 m away from the tunnel centerline were completed. In the experiments, the tracked drilling and anchoring robot exhibited a significant difference in speed between the two sides of the tracks with a track skid rate of 0.22. Although the real-time tracking maximum error in the y-direction from the tunnel centerline was 0.13 m, the final error was 0.003 m, meeting the requirements for position deviation control of the drilling and anchoring robot in tunnel environments. These research findings provide a theoretical basis and technical support for the intelligent control of tracked mobile devices in coal mine tunnels, with significant theoretical and engineering implications. Full article

This article concerns the modeling and motion control of a mobile robot with six independently driven and non-steered wheels. The main research issue is analyzing the influence of the structure of the control system and wheel track on the control accuracy and energy efficiency during robot motion on horizontal paved ground. For this purpose, the kinematic relationships for the robot are discussed and a simplified dynamics model for control applications is developed. The robot’s dynamics model takes into account the most important phenomena of the wheel interaction with the paved ground, including slip. In addition, it is supplemented with a model of the robot’s drive units. Two versions of the control system were adopted for analysis, i.e., with the wheels’ controller only and additionally equipped with a pose controller. Simulation studies were carried out for the developed robot dynamics model and the analyzed versions of the control system in order to investigate the influence of the track width of the wheels and the structure of the control system on motion accuracy and energy efficiency. In order to quantitatively compare the results for the analyzed solutions, quality indices were introduced. The results of the simulation research indicate the influence of the track width of the wheels on the accuracy of motion when using the wheels’ controller, as well as its impact on energy efficiency. Moreover, they show that it is possible to significantly improve the accuracy of motion by using an additional pose controller, which allows limiting the impact of the non-optimal geometric parameters of the robot and the slip of the wheels on trajectory tracking errors. However, the addition of the pose controller does not significantly affect the energy efficiency during the robot’s motion, which may be even worse in this case. Full article

Climbing manufacturing robots can create a revolutionary manufacturing paradigm for large and complex components, while the motion control of climbing manipulation-oriented robots (CMo-Rs) is still challenging considering anti-slippage problems. In this study, a CMo-R with full-scenery climbing capability and redundant load-bearing mobility is designed based on magnetic adsorption. A four-wheel kinematic model considering the slipping phenomenon is established. An adaptive kinematic control algorithm based on slip estimation using Lyapunov theory is designed for uncertain inclined planes. For comparison, the traditional PID-based algorithm without slip consideration is implemented as well. Numeric simulations are conducted to tackle the trajectory tracking problems for both circular and linear trajectories on the horizontal plane (HP), 50° inclined plane (50° IP), 60° inclined plane (60° IP), and vertical plane (VP). The results prove that our approach achieves better tracking accuracy. It demonstrated applicability in various climbing scenarios with uncertain inclined planes. The results of experiments also validate the feasibility, applicability, and stability of the proposed approach. Full article