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When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University's Center for Advanced Vehicular Systems. [ABSTRACT FROM AUTHOR]

This work presents a finite-time robust path-following control scheme for perturbed autonomous ground vehicles. Specifically, a novel self-tuning nonsingular fast-terminal sliding manifold that further enhances the convergence rate and tracking accuracy is proposed. Then, uncertain dynamics and external disturbances are estimated by a high-gain disturbance observer to compensate for the designed control input. Successively, a super-twisting algorithm is incorporated into the final control law, significantly mitigating the chattering phenomenon of both the input control signal and the output trajectory. Furthermore, the global finite-time convergence and stability of the whole proposed control algorithm are proven by the Lyapunov theory. Finally, the efficacy of the proposed method is validated with comparisons in a numerical example. It obtains high control performance, reduced chattering, fast convergence rate, singularity avoidance, and robustness against uncertainties. [ABSTRACT FROM AUTHOR]

Autonomous Ground Vehicles play a vital role in various industries, necessitating efficient route planning for mission success without human intervention. This study examines two metaheuristic optimization approaches, Genetic Algorithms and Ant Colony Optimization, within the context of route planning for these robotic entities. The comprehensive methodology utilizes the designs of these metaheuristic methods, and a comparative analysis is conducted through simulated experiments in diverse environments. Thefindings unveil that Genetic Algorithms excel in simple terrains; while Ant Colony Optimization demonstrates robust exploration capabilities in complex environments with obstacles and additional constraints. This underscores the importance of selecting the appropriate metaheuristic based on specific mission requirements and environmental conditions. The research furnishes insights to designers and operators, enabling informed decisions in route planning strategy selection, thereby enhancing the efficiency and safety of autonomous robotic operations across various scenarios. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) and approximate dynamic programming (ADP) have been recently studied to solve nonlinear optimal control problems (OCPs) of continuous‐time (CT) systems. However, online learning efficiency and reliability are two major concerns to be further improved. Motivated by the above issues, in this paper we propose a receding‐horizon reinforcement learning (RHRL) algorithm for near‐optimal control of CT systems under control constraints. Different from classic RL and ADP, in the proposed approach, the infinite‐horizon OCP is decomposed as a series of finite‐horizon ones solved with an actor‐critic structure according to the receding horizon strategy, which can improve the online learning efficiency and reliability. The unknown dynamics of the system are identified offline using a sparse kernel‐based neural network structure whose weights are also updated online in the RHRL framework to improve the control performance. Moreover, the convergence of the modeling error is proven. To verify the effectiveness of our approach, we apply the RHRL algorithm to the autonomous ground vehicle for realizing near‐optimal path‐tracking control. Compared with CT model predictive control using a nominal model and other model‐free tracking controllers such as pure pursuit, heuristic dual programming, and the soft actor‐critic algorithm, RHRL performs better in terms of control performance. [ABSTRACT FROM AUTHOR]

The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented. The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model's applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain. • A 6-DOF dynamic vehicle model is used for autonomous driving in terrain. • The force models are modified considering 3D terrain. • CDEKF with fixed-lag smoothing is used for online state estimation. • State estimation is demonstrated in RTK-GNSS denied areas. • The 3D form of the future path is used in state predictions using the dynamic model. [ABSTRACT FROM AUTHOR]

Over the past several decades, the automobile industry has denoted significant research efforts to developing in‐wheel‐motor‐driven autonomous ground vehicles (IWM‐AGVs) with active front‐wheel steering. One of the most fundamental issues for IWM‐AGVs is path following, which is important for automated driving to ensure that the vehicle can track a target‐planned path during local navigation. However, the path‐following task may fail if the system experiences a stuck fault in the active front‐wheel steering. In this paper, a fault‐tolerant control (FTC) strategy is presented for the path following of IWM‐AGVs in the presence of a stuck fault in the active front‐wheel steering. For this purpose, differential steering is used to generate differential torque between the left and right wheels in IWM‐AGVs, and an adaptive triple‐step control approach is applied to realize coordinated lateral and longitudinal path‐following maneuvering. The parameter uncertainties for the cornering stiffness and external disturbances are considered to make the vehicles robust to different driving environments. The effectiveness of the proposed scheme is evaluated with a high‐fidelity and full‐car model based on the veDYNA‐Simulink joint platform. [ABSTRACT FROM AUTHOR]

A priori knowledge about the height profile of the path is vital for rollover avoidance in the context of autonomous driving through uneven forest ground. The forest ground is usually covered with either soft vegetation in summertime, or by snow in winter. Thus, the exact solid form of the forest ground cannot be detected by camera or LiDAR. This article, we propose height-odometry and aided height-odometry methods for ground height estimation. The height-odometry method depends solely on interoceptive and proprioceptive sensor data, while the aided height-odometry combines height-odometry output with the existing 3D map information. Thus, the central idea is to build a reference 3D path for autonomous forest machines where the spatial positioning – based on the RTK-GNSS or Forest SLAM method – is fused with the output of (aided) height-odometry method(s). We evaluate the proposed height-odometry methods in two separate environments that are accurately (3D) mapped by a UAV using the advanced machine-vision-based SfM method and the LiDAR-based SLAM algorithms. Through comprehensive data analysis, we demonstrate that the proposed 3D path estimation methods are practical and simple to implement, yet sufficient to estimate the height profile of the path with desired accuracy. • A priori knowledge of the ground's elevation profile is crucial for autonomous harvesting operations. • The height-odometry and aided height-odometry methods are introduced to compute the ground's elevation profile. • Our methods rely on the vehicle's geometry, IMU data, wheel height measurement, and a sparse a priori 3D map. • The reference elevation models are created using sophisticated methods involving drone mapping. • The performance of the proposed height estimation methods is evaluated against reference methods in both open and covered areas. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an optimized rapidly exploring random tree A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is, therefore, a promising method in achieving autonomous ground vehicle navigation in partially known environments. [ABSTRACT FROM AUTHOR]

Over the past several decades, the automobile industry has devoted much significant research efforts to developing autonomous ground vehicles (AGVs). One of the most fundamental issues for AGVs is path following, which is concerned with the control strategy for AGVs to follow the scheduled paths. This paper presents a new path following control approach, where an output-feedback triple-step controller is designed to realize coordinated lateral and longitudinal control without a measurement of lateral velocity. The main contribution of this paper is the integrated design of the observer and control gains in the framework of Lyapunov stability and input-to-state stability (ISS) theory. The effectiveness of the proposed control system has been evaluated through simulations. [ABSTRACT FROM AUTHOR]

We compare two models of two-level facility location problems for network design in autonomous ground vehicle (AGV) operations. The two-level model consists of locations for charging stations (main facilities), as well as for storage locations (substations). Demand points will represent processing locations. In both formulations, demands are assigned to substations and substations are assigned to main facilities. The formulations differ in whether each connection between a facility and a substation is counted once in absolute terms, or once per demand. These represent two different views, in which transfer between a main facility and substation is carried out either in bulk, e.g. using a shuttle, or by each AGV independently. Selected experimental results are presented for geometric networks and networks consisting of uniformly distributed points on a regular mesh. These results indicate that the two formulations lead to vastly different network designs in terms of the number of facilities and connectivity. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term ‘unstructured’ in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Featured Application: This work presents an MPC scheme for stabilization control of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, this scheme is able to maintain handling stability by preventing excessive sideslip and rollover while ensuring collision-free trajectories. Such an MPC scheme can not only contribute to the performance of AGVs, but also be used as an advanced safety technique in advanced driver-assistance systems (ADAS) and intelligent transportation systems (ITS). This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This study proposes a novel integrated local trajectory planning and tracking control (ILTPTC) framework for autonomous vehicles driving along a reference path with obstacles avoidance. For this ILTPTC framework, an efficient state-space sampling-based trajectory planning scheme is employed to smoothly follow the reference path. A model-based predictive path generation algorithm is applied to produce a set of smooth and kinematically-feasible paths connecting the initial state with the sampling terminal states. A velocity control law is then designed to assign a speed value at each of the points along the generated paths. An objective function considering both safety and comfort performance is carefully formulated for assessing the generated trajectories and selecting the optimal one. For accurately tracking the optimal trajectory while overcoming external disturbances and model uncertainties, a combined feedforward and feedback controller is developed. Both simulation analyses and vehicle testing are performed to verify the effectiveness of the proposed ILTPTC framework, and future research is also briefly discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller. [ABSTRACT FROM AUTHOR]

A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the practicality of its application with both matched and unmatched uncertainties considered in the controller design. The controller is designed using an offset model derived from the kinematic model and its operation is verified by simulation and field experiments. In the simulations, the kinematic model based controller is used to control both a kinematic model and a dynamic model of a tractor to verify the performance of the kinematic model based controller. The proposed controller is compared with two other nonlinear controllers, namely, back stepping control and model predictive control. In the field experiments, the three controller were used to control the physical tractor to follow a specified path. Simulation and experimental results are presented to show that the proposed controller demonstrated the required robustness and accuracy at all times. [ABSTRACT FROM AUTHOR]

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control problem of steer-by-wire autonomous ground vehicle considering the complete failure of vehicle steering motor. In order to achieve the trajectory tracking control of autonomous ground vehicle in presence of the complete failure of vehicle steering motor, the mechanical transmission mechanism of vehicle steering system is analyzed with the steering motor fault being considered, and the differential steering is used to actuate the turning of vehicle in case of emergency. A model predictive controller with the constraints of tire cornering angle and road adhesion is designed to compute the expected front-wheel steering angle for vehicle trajectory tracking control, and a nonsingular terminal sliding mode controller is proposed to track the expected front-wheel steering angle using differential-steering moment, in which a novel observer design method is presented to estimate the actual front-wheel steering angle. Two case studies are implemented in a CarSim-Simulink co-simulation platform, and the simulation results have verified that the proposed method have satisfactory trajectory tracking control effectiveness in presence of the complete failure of vehicle steering motor. [ABSTRACT FROM AUTHOR]

• A cascaded ACF scheme is proposed to accurately estimate longitudinal and lateral velocities by considering tire force coupling. • Experiments on a scaled FWIA in-wheel-motor-driven EAV have been conducted to verify the effectiveness of the proposed ACF approach. • The ACF estimator-based path planning and following control with MPC strategies are originally designed for PIC and for FWIA EAVs. This paper investigates a post-impact control (PIC) method for four-wheel independently actuated (FWIA) electric autonomous vehicles (EAVs) after an initial impact. Differential steering angles actuated by differential torques generated from the left and right wheels have been utilized as an inherent redundant control strategy when the steering motor totally fails, to realize the PIC and secondary collision mitigation. To this end, a vehicle state estimation-based PIC strategy is developed in this work, and three contributions have been made as follows: 1) A novel cascaded estimation approach using the adaptive complementary filters (ACF) is proposed to estimate the longitudinal and lateral velocities with low-cost measurement; 2) Experiments on a scaled FWIA EAV with differential steering mechanism have been conducted to verify the proposed ACF approach, indicating that ACF can accurately estimate the longitudinal and lateral velocities; 3) The vehicle velocity estimation-based path planning and following with model predictive control (MPC) strategy are proposed for PIC and for FWIA EAVs, both for the first time. Finally, the verifiable simulation based on the high-fidelity CarSim-Simulink conjoint platform with the experimentally identified vehicle parameters has been conducted, which has verified the proposed ACF-based PIC strategy can effectively avoid the secondary collision and guarantee vehicle stability and control performance. [ABSTRACT FROM AUTHOR]