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The adoption of agricultural robots is revolutionizing the agricultural sector, offering innovative solutions to optimize production and reduce environmental impact. This review examines the main functions and applications of agricultural robots, with a focus on the crops handled and the technologies employed. The study analyzes the current state of the art regarding the market trend of agricultural robots used in field and greenhouse operations. Several solutions are emerging, some already implemented and others still in the prototype or project stage. These solutions are beginning to spread, though they may still seem far from widespread field application, particularly given the peculiarities and heterogeneity of the global agricultural landscape. In the face of the many benefits associated with the use of agricultural robots, even today some technical bottlenecks and costs limit their widespread use by farmers. The review provides a fairly comprehensive and up-to-date overview of current trends in agricultural automation, suggesting new areas of research to improve the efficiency and adaptability of robotic systems to different types of crops and environments. [ABSTRACT FROM AUTHOR]

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 driving in unstructured environments is crucial for various applications, including agriculture, military, and mining. However, research in unstructured environments significantly lags behind that in structured environments, mainly due to the challenges posed by harsh environmental conditions and the intricate interactions between vehicles and terrains. This article first categorizes unstructured path planning into hierarchical and end-to-end approaches and then the special parts compared to structured path planning are emphatically reviewed, such as terrain traversability analysis, cost estimation, and terrain-dependent constraints. This article offers a comprehensive review of the relevant factors, vehicle–terrain interactions, and methods of terrain traversability analysis. The estimation methods of safety cost, energy cost, and comfort cost are also emphatically summarized. Moreover, the constraints caused by the limits of terrains and vehicles are discussed. The applications of algorithms in recent articles for path planners are reviewed. Finally, crucial areas requiring further research are analyzed in unstructured path planning. [ABSTRACT FROM AUTHOR]

This research aims to compare autonomous ground vehicles with conventional and electric vans on the basis of associated vehicle costs and benefits related to their use, taking into account economic feasibility. Cost per vehicle kilometre is derived using the total cost of ownership method adjusted with the inclusion of labour costs and the impact of solar panel application on fuel efficiency while travel time-related and capacity occupations and reliability benefits serve as a basis for the total possible number of parcels delivered. The results show that, under the current structural and infrastructural conditions of urban delivery, the experimental model can be potentially successful in terms of cost per kilometre (0.133/km) but not as effective in terms of the total possible number of parcels delivered. This study defines autonomous ground vehicles with lockers as an innovative last mile solution and contributes to the academic literature by investigating the concept's efficiency competitiveness. [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]