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In this study, the design and driving performance evaluation of a driving system for driving on deformable terrain was performed using terramechanics theory and multi-body dynamics simulation. For the design of the driving system, the mechanical interaction of track-terrain was analyzed using a Bekker-based model. Based on the analyzed results, the design of a track suitable for the deformable terrain to be driven and selection of a power source (engine, transmission, etc.) were carried out. A multi-body simulation model of the tracked vehicle reflecting the designed track and the selected power source was developed, and a ground model reflecting the mechanical property of terrain was also developed to analyze the mechanical interaction of the track-terrain through simulation. In addition, each link constituting the track was modeled as a 6 DOF spring/damper system to consider the track's tension force and load distribution under the track, and through this, different ground pressure and soil thrust were applied according to the motion state of each link. Finally, driving performance analyses were performed using the developed tracked vehicle, and as a result, it was confirmed that the driving requirements of the tracked vehicle were satisfied.

This study presents sensitivity-based adaptive model-free adaptive displacement and velocity control algorithms for unknown single-input multi-output (SIMO) systems using recursive least squares (RLS) with exponential forgetting. Nonlinearities in real-world systems make predictions of the dynamic behaviors of systems challenging and have a negative influence on the performance of control systems. To address this problem, the sensitivity-based model-free adaptive control algorithm, which does not require a mathematical model of a system, is proposed in this study. In the algorithm, a feedback control law for a SIMO system was designed with multiple adaptive feedback gains. The virtual function that represents the relationship between multiple feedback gains and control errors was also designed to construct an adaptation rule with a linear combination. The coefficients in the virtual function were estimated in real-time using RLS with multiple exponential forgetting factors. By using the estimated coefficients, the adaptation rule for feedback gains with the gradient descent method was designed, which can be automatically adjusted through applying Lyapunov’s direct method. The performance of the proposed model-free adaptive control algorithm was evaluated in an actual DC motor system under angular displacement and velocity tracking control scenarios. Evaluation results show that the designed control algorithm enables the system to track the desired output successfully without system modeling.

Automated excavation is hard to achieve due to several inherent problems such as resistive force acting against the bucket, non-homogenous dynamics of various excavation media, and nonlinearities of the excavator’s hydraulics system. To deal with this issue, this paper provides an integrative control strategy for successful autonomous excavation that considers the mutually associated factors, i.e., position, contour, and force control. For the position tracking, a non-linear PI controller was designed to track the position of individual actuators of the excavator and thereby control the bucket tip’s position. In addition, the contour control technique was applied to achieve an optimal excavation path to minimize contour errors. Finally, to compensate for the ground resistive force during digging tasks, a force impedance controller was designed along with the time-delayed control that reduces the effect of dynamic uncertainties. Experimental results with a modified mini-wheeled excavator show that the developed integrative tracking control strategy can provide a comprehensive solution to improving the tracking performance for autonomous excavation that can simultaneously deal with the critical components of position, contour, and force control.

In this paper, a self-tuning rule-based position control algorithm is proposed for DC motors with system parameter estimation using the recursive least squares method. First, a mathematical model of the angular position control of a DC motor was derived. Next, the time-varying parameters including the rotational inertia in the model were estimated using the RLS method along with multiple forgetting factors without prior knowledge of the system. Based on the derived model and the parameter estimation, a sliding mode control algorithm was designed by applying a self-tuning rule that enables the magnitude of the voltage input to be adaptively adjusted for improvement of the energy efficiency. The performance of the designed control algorithm was then experimentally evaluated under several different load conditions. Finally, the evaluation results show that the designed controller achieves a satisfactory capability for a DC motor to deal with both tracking accuracy and energy efficiency without prior knowledge of the system.

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

During the winter season, snowplowing has a significant effect on road users as it is critical to winter road maintenance and operations. The main goal of this study is to generate optimal routes for snowplowing trucks for efficient road maintenance. In addition to the conventional problem of reducing travel time and distance, this study also incorporates actual operational constraints, such as minimum maintenance standards and driver safety, to improve the overall efficiency of operations. To achieve the objectives, we first implemented the Chinese Postman Problem (CPP) to create Euler circuits from the initial routes and then identified the shortest paths by applying Dijkstra’s algorithm. Then, the Tabu search algorithm was chosen as a metaheuristic algorithm for the optimization process that finds near-optimal solutions by considering operational constraints for snowplow routes. Unsafe turning conditions and minimum maintenance standards were taken into account in the objective function defined for the optimization process. In simulations, the route obtained by our approach was compared to one with the application of CPP only in terms of travel distance, time, turning conditions, and road maintenance priority.

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.

This study focuses on the development and evaluation of advanced safety algorithms for excavators using virtual reality (VR). An excavator typically operates under a stationary state with its working parts rotating 360° in coordination with nearby workers. During excavation, a fatal accident can occur due to operator carelessness and work site blind spots. Accidents due to collisions with nearby workers have been increasing. Accordingly, we presented safety system algorithms to prevent collisions with surrounding objects and secure the maximum working area in this study. We also evaluated the performance of safety system algorithms using VR. For risk assessment, we calculated the predicted working area through a kinematic analysis of the excavator’s working parts and accordingly conducted target selection of risk factors. We used time-to-collision and warning indices as safety indices for the safety assessment of the selected target and divided the excavator’s working modes into three categories: Safe, warning, and emergency braking modes. Control inputs, such as alarms and braking, were appropriately defined for each working mode. Under warning mode, workers can avoid collisions because a safety system will alert them of dangerous situations through an alarm. Under emergency braking mode, an emergency braking input signal is dispatched with an alarm to automatically prevent collisions. The advanced safety algorithm proposed in the study was developed in MATLAB/Simulink environment. The VR application was developed using a physics engine. On the basis of this application, the performance evaluation of the safety system algorithms was conducted on the frequently occurring sticking scenario.

For transient analysis of structural dynamics with reduced order model (ROM), data recovery procedures that use modal methods such as the classical mode-acceleration (MA) and modal-displacement (MD) methods are an important step in order to increase the convergence and accuracy of the solution. In this work, we propose novel MA and MD methods for highly accurate transient analysis with a reduced order model based on enhanced Craig–Bampton (ECB) formulation, which is an extension of the classical Craig–Bampton (CB) method that is a way to reduce the size of a finite element (FE) model. The performance of the proposed data recovery approach is demonstrated with two numerical examples. We also investigate the relation between the proposed and classical MA and MD methods.

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip&rsquo, s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.

The Excavator is one of the key equipment utilized for earthmoving tasks at construction sites. This paper aims to provide an integrative tracking control strategy comprising of position, contour, and force tracking controls for excavation tasks. A non-linear proportional-integral controller was applied for position control of hydraulic actuators and the contour control strategy was added to create an optimal path of the bucket tip minimizing contour errors. The force control was finally considered to compensate for the ground resistive force. A multiphysics simulation model was developed for an evaluation of the designed controller’s performance through co-simulation. Experimental results obtained from a test platform show that the developed control algorithms provide good tracking performance for soil digging.

The study deals with the design of control algorithms to enhance the steering performance of all-terrain cranes. For this, its hydraulic steering system was modeled using a multiphysics simulation tool and the self-tuning fuzzy PID controller was developed to control hydraulic steering actuators with nonlinearities. The designed control algorithm was validated through co-simulation using a real-time simulator and showed satisfactory performance.

This study proposes an effective control strategy for autonomous excavation under complex ground conditions, by integrating position, contour, and force control that are mutually associated factors. For the position control strategy, a non-linear PI controller was devised to control the stroke of each hydraulic cylinder, and therefore the bucket tip's position. To compensate for the ground resistive forces in contact space, an impedance controller was designed. Finally, contour compensation was considered to generate an optimal path of the bucket tip for ground leveling tasks. The performance of developed control algorithms was evaluated in the case of ground leveling task through co-simulation in multi-physics domains. Simulation results show that the designed control scheme provides good results in terms of transient response and tracking accuracy by dealing with all the aspects of force, position, and contour compensation.

This paper proposes sensing and safety techniques for autonomous excavators working on construction sites. In autonomous construction equipment, safety is a significant concern to reduce fatal accidents and machinery damage. In taking up this point of view, this paper deals with object tracking, motion prediction, and track management for detected objects that can improve the safety function of autonomous construction equipment. By using the info of predicted motion of detected objects and actual working areas of an excavator, and calculating safety indices, the proposed safety algorithms can evaluate the degree of safety (i.e., potential collision risks).

This paper presents a rotational inertia estimation algorithm for excavators based on recursive least-squares with forgetting and an adaptive updating rule that uses the performance analysis of the Kalman filter. Generally, excavators execute a swing motion with various materials, and the rotational inertia of the excavator is changed greatly due to the excavator’s working posture. The large variation in the rotational inertia of the excavator has an influence on the dynamic behaviors of the excavator, and an estimation of the excavator’s rotational inertia is essential to developing a safety system based on prediction of dynamic behavior. Therefore, a real-time rotational inertia estimation algorithm has been proposed in this study using a swing dynamic model. The proposed estimation algorithm has been designed using only swing velocity, utilizing the recursive least squares method with multiple forgetting for practical application to actual excavators. Two updating rules have been applied to the estimation algorithm in order to enhance the estimation performance. The first proposed rule is the damping coefficient updating rule. The second rule is the forgetting factor updating rule based on real-time analysis of linear Kalman filter estimation performance. The performance evaluation of the estimation algorithm proposed in this paper has been conducted based on the excavator’s typical dumping scenario. The performance evaluation results show that the developed inertia estimation algorithm can estimate actual rotational inertia with the two designed updating rules using only excavator swing velocity.

This paper presents a laser scanner–based predictive safety system for excavators. Blind spots on excavators and operators’ carelessness cause majority of fatal accidents, such as those in which de...

This research deals with assist wheel control to improve the steering efficiency and dynamic stability of the multi-axle crane based on model predictive control. Since multi-axle crane has relatively high inertia and long distance between the axles, it has slow dynamic response, and thus, different steering strategies according to driving speed intervals are required. Specifically, the steering strategy is that the number of wheels fixed mechanically increases to secure dynamic stability as the driving speed increases. However, although this strategy enables to secure stability by slowing down the dynamic response, it also has a weakness to decrease the steering efficiency. If the steering efficiency is decreased, it may result in augmenting an accident rate due to an increase in driver’s fatigue. Therefore, this study suggests a new steering strategy to improve the steering efficiency by simultaneously guaranteeing dynamic stability. The suggested steering control algorithm can enhance both steering efficiency and stability by deriving an assist wheel control input based on model predictive control using a variable weighting factor derived from the driver’s steering input (steering angle on the first axle). Development and performance evaluation of the suggested algorithm were conducted in the MATLAB/Simulink environment, and evaluation results confirmed that the developed algorithm could both enhance the driver’s steering efficiency and secure dynamic stability.

This paper describes stability assist wheel control based on RLS estimation with forgetting in order to improve the dynamic stability of a multi-axle all-terrain crane. Existing multi-axle all-terrain cranes have greater mass and a longer distance between axles than ordinary vehicles, so their rotational inertia is very large. Large rotational inertia implies a slower dynamic response for yaw motion, and it is difficult to achieve the desired yaw motion within an expected amount of time. Therefore, ensuring yaw and lateral dynamic stability is an important theme in research regarding multi-axle all-terrain cranes. In this study, to ensure the crane’s dynamic stability, a simplified linear crane model of a multi-axle all-terrain crane was developed, in which an assist wheel was chosen according to speed, for control. To improve the driving stability, the chosen assistant wheel’s optimal steering angle was calculated through LQR, and to calculate the optimal feedback gain and steering angle, the rotational inertia and lateral velocity were estimated using recursive least square algorithms with forgetting. MATLAB/Simulink based simulations were used to evaluate the performance of the assist wheel controller for improving the crane’s dynamic stability, and the simulation results showed that the proposed stability assist wheel control method improved yaw and lateral dynamic stability over existing steering systems.

In a conventional steering system for a multi-axle crane, the steering angle of each axle is determined according to Ackermann’s steering principle, which minimizes the slip angles of the tires. The role of optimal steering control in improving a driver’s steering efficiency is hardly considered in Ackermann’s principle. To address this problem, this paper proposes a control strategy for determining the optimal steering angles for a multi-axle crane and thereby improving a driver’s steering efficiency by applying the model predictive control (MPC) algorithm and defining a driver’s intentions. A simplified crane model for the steering system was developed using a bicycle model, and a comparative study was carried out via simulation to analyze steering performance for the conventional (Ackermann) and proposed steering control systems for the cases of all-wheel steering and road steering modes. The simulation results show that both the minimum turning radius and the driver’s steering effort are decreased more by the proposed steering control system than by conventional system and that the proposed control strategy therefore yields better steering performance.