Search Results (23)

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

The interactions between ground reaction forces and inter-unit coupling forces add complexity to the study of the turning motion of articulated tracked vehicles (ATVs). To accurately analyze the turning performance of an ATV, this study developed a steady-state steering model that captures the effects of load transfer caused by coupling and centrifugal forces. First, based on vehicle kinematics under skidding conditions, formulas that incorporate parameters for the lateral track displacement were derived to calculate the turning radii of the front and rear units. Then, the track traction forces and turning resistance moments were calculated using the shear stress–shear displacement relationship. Finally, a steady-state steering model on firm ground conditions was developed for the vehicle according to mechanical equilibrium conditions, and the model was validated using previously reported data. Analyses of the results revealed that the coupling forces provided the driving moments for the turning motion by the transfer of the centrifugal and ground reaction forces that acted on the front and rear units. During turning, the rear unit had a larger radius than the front unit, and the minimum swept radius of the ATV was dependent upon the radius of the outer track trajectory of the rear unit. Specifically, at a speed of 3.1 m/s and a steering angle of 35°, the vehicle exhibited a minimum outer swept radius of 8.8 m, requiring a turning space equivalent to a 3.1-m-wide road. The required turning space increased as both the steering angle and speed increased. Full article

Skid-steered technology has been widely applied to wheeled vehicles due to its advanced pivot steering capabilities and adaptable power transmission systems. To better understand the steering characteristics of skid-steered wheeled vehicles and derive general conclusions, a torque differential-based (TD-based) two-degree-of-freedom (2-DOF) dynamic model was developed. This model is grounded in the vehicle’s steering mechanism and the single-wheel dynamics model, and a steering radius model based on TD input was established using the concept of the instantaneous center of rotation (ICR). Additionally, an expression for the stability factor was derived, and both the steady-state and transient steering characteristics were analyzed. Finally, the real vehicle tests demonstrated that the TD-based dynamic model responds quickly, maintains high precision, and remains stable across different steering input frequencies. Compared with the SD-based dynamic model, the TD-based dynamic model has a 2.553% higher calculation accuracy for the steering radius, a 6.251% higher comprehensive response accuracy for the steering input, and a response speed advantage of 1.035%. 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

Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%. Full article

This paper proposes a neural network-based actuator fault detection scheme for four-wheeled skid-steered unmanned ground vehicles (UGV). The neural network approach is first validated on vehicle dynamics simulations. Then, it is tailored for the experimental setup. Experiments involve a motion tracking system, Husarion Rosbot 2.0 UGV with associated network control systems. For experimental work, the disturbance is intentionally induced by augmenting wheels with a bump. Network size optimization is also carried out so that computing resource is saved without degrading detecting accuracy too much. The resulting network exhibit fault detection and isolation accuracy over 97% of the test data. A scenario is experimentally illustrated where a fault occurs, is detected, and tracking control is modified to continue operation in the presence of an actuator fault. Full article

Designing controllers for skid-steered wheeled robots is complex due to the interaction of the tires with the ground and wheel slip due to the skid-steer driving mechanism, leading to nonlinear dynamics. Due to the recent success of reinforcement learning algorithms for mobile robot control, the Deep Deterministic Policy Gradients (DDPG) was successfully implemented and an algorithm was designed for continuous control problems. The complex dynamics of the vehicle model were dealt with and the advantages of deep neural networks were leveraged for their generalizability. Reinforcement learning was used to gather information and train the agent in an unsupervised manner. The performance of the trained policy on the six degrees of freedom dynamic model simulation was demonstrated with ground force interactions. The system met the requirement to stay within the distance of half the vehicle width from reference paths. Full article

Skid steers are versatile self-propelled machines that are regularly used in a variety of recreational applications and occupational industries. They can be hazardous for both operators and bystanders. The purpose of this paper is to describe patterns of skid steer injuries in the US from 2015 to 2020. Data were obtained from Occupational Safety and Health Administration (OSHA) accident reports and the severe injury database. Agriculture-related incidents were obtained from AgInjuryNews. The study identified 312 skid steer-related injuries (2015–2020) in OSHA, with an additional 68 agricultural injuries identified using AIN. Construction, administrative and waste management, and agriculture industries were the top three industries with the highest number of injuries. Bystander workers experienced a higher number of injuries than operators. Contact with the machine was the most prevalent and more fatal than the other injury events. Agricultural skid steer injuries involved a broad age range of victims, from very young children to adults. These findings emphasize the need for improved safety engineering and clear safety guidelines for skid steer operators and those who are around skid steers. With the increased prevalence of skid steers across industries, it is imperative to have cohesive and comprehensive safety regulations, guidelines, and policy enforcement. Full article

Due to the advantages of their drive configuration form, skid-steering vehicles with independent wheel drive systems are widely used in various special applications. However, obtaining a reasonable distribution of the driving torques for the coordinated control of independent driving wheels is a challenging problem. In this paper, we propose a torque distribution strategy based on the Knowledge-Assisted Deep Deterministic Policy Gradient (KA-DDPG) algorithm, in order to minimize the desired value tracking error as well as achieve the longitudinal speed and yaw rate tracking control of skid-steering vehicles. The KA-DDPG algorithm combines knowledge-assisted learning methods with the DDPG algorithm, within the framework of knowledge-assisted reinforcement learning. To accelerate the learning process of KA-DDPG, two assisted learning methods are proposed: a criteria action method and a guiding reward method. The simulation results obtained, considering different scenarios, demonstrate that the KA-DDPG-based torque distribution strategy allows a skid-steering vehicle to achieve high performance, in tracking the desired value. In addition, further simulation results, also, demonstrate the contributions of knowledge-assisted learning methods to the training process of KA-DDPG: the criteria action method speeds up the learning speed by reducing the agent’s random action selection, while the guiding reward method achieves the same result by sharpening the reward function. Full article

In this paper, a new methodology for the numerical simulation of the wheel–surface interaction has been presented. The finite-element method was combined with the discrete-element method, rigid body dynamics, and the advanced wheel–surface friction model. Compared to the current state-of-the-art, this novel approach can more realistically model the application of the traction force on the contact surface between the wheel and the soil. The rotation of a non-driven wheel is caused by the movement of the axis and the contact forces. The method that has been developed is able to assess both the longitudinal and lateral forces for a wide range of attack angles of the wheel; this is essential for calculating the traction effort of skid-steered vehicles. Full article

Meta-reinforcement learning (meta-RL), used in the fault-tolerant control (FTC) problem, learns a meta-trained model from a set of fault situations that have a high-level similarity. However, in the real world, skid-steering vehicles might experience different types of fault situations. The use of a single initial meta-trained model limits the ability to learn different types of fault situations that do not possess a strong similarity. In this paper, we propose a novel FTC method to mitigate this limitation, by meta-training multiple initial meta-trained models and selecting the most suitable model to adapt to the fault situation. The proposed FTC method is based on the meta deep deterministic policy gradient (meta-DDPG) algorithm, which includes an offline stage and an online stage. In the offline stage, we first train multiple meta-trained models corresponding to different types of fault situations, and then a situation embedding model is trained with the state-transition data generated from meta-trained models. In the online stage, the most suitable meta-trained model is selected to adapt to the current fault situation. The simulation results demonstrate that the proposed FTC method allows skid-steering vehicles to adapt to different types of fault situations stably, while requiring significantly fewer fine-tuning steps than the baseline. Full article

Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably. Full article

Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified. Full article

The efficiency of a skid-steer, all-wheel drive, multiple-axle vehicle with a hydrostatic drivetrain equipped with low-speed motors when it operates on soft terrain was studied. A flow divider enables a single pump to simultaneously power more than one motor circuit with different pressures in each. It prevents kinematic discrepancy and improves vehicle mobility. There are two types of flow divider: spool type and gear type, where each type has its own set of performance characteristics, such as flow range, pressure drop, accuracy and application parameters. In the present work, the influence of the characteristics of both types of flow divider on overall vehicle driveline efficacy is described. Full article

Articulated tracked vehicles have been traditionally studied and appreciated for the extreme maneuverability and mobility flexibility in terms of grade and side slope capabilities. The articulation joint represents an attractive and advantageous solution, if compared to the traditional skid steering operation, by avoiding any trust adjustment between the outside and inside tracks. This paper focuses on the analysis and control of an articulated tracked vehicle characterized by two units connected through a mechanical multiaxial joint that is hydraulically actuated to allow the articulated steering operation. A realistic eight degrees of freedom mathematical model is introduced to include the main nonlinearities involved in the articulated steering behavior. A linearized vehicle model is further proposed to analytically characterize the cornering steady-state and transient behaviors for small lateral accelerations. Finally, a hitch angle controller is designed by proposing a torque-based and a speed-based Proportional Integral Derivative (PID) logics. The controller is also verified by simulating maneuvers typically adopted for handling analysis. Full article