Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments.

Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking control navigation methods. However, the inherent complexity of orchard environments presents substantial challenges for robotic systems. Researchers have extensively investigated the robustness of various path planning and tracking control techniques for OMR in complex scenes, aiming to improve the robots’ security, stability, efficiency, and adaptability. This paper provides a comprehensive review of the state-of-the-art path planning and tracking control strategies for OMR in such environments. First, it discusses the advances in both global and local path planning methods designed for OMR navigating through complex orchard scenes. Second, it examines tracking control approaches in the context of different motion models, with an emphasis on the application characteristics and current trends in various scene types. Finally, the paper highlights the technical challenges faced by OMR in autonomous tasks within these complex environments and emphasizes the need for further research into navigation technologies that integrate artificial intelligence with end-to-end control systems. This fusion is identified as a promising direction for achieving efficient autonomous operations in orchard environments.

With the advancement of precision agriculture, the autonomous navigation of unmanned agricultural ground vehicles (UAGVs) has emerged as a critical research topic. As a fundamental component of autonomous navigation, path-tracking control is essential for ensuring the accurate and stable operation of UAGVs. However, achieving high-precision and robust tracking in agricultural environments remains challenging due to unstructured terrain, variable wheel slip, and complex dynamic disturbances. This review provides a structured and comprehensive survey of modeling and control methodologies for UAGVs, with particular emphasis on control-theoretic formulations and their applicability across diverse agricultural scenarios. In contrast to prior reviews, the modeling approaches are systematically classified into geometric, kinematic, and dynamic models, including extended formulations that incorporate wheel slip and external disturbances. Furthermore, this paper systematically reviews commonly adopted path-tracking strategies for UAGVs, including proportional–integral–derivative (PID) control, pure pursuit (PP), Stanley control, sliding mode control (SMC), model predictive control (MPC), and learning-based approaches. Emphasis is placed on their theoretical underpinnings, tracking accuracy, adaptability to unstructured field environments, and computational efficiency. In addition, several key technical challenges are identified, such as terrain-adaptive vehicle modeling, slip compensation mechanisms, real-time implementation under hardware constraints, and the cooperative control of multiple UAGVs operating in dynamic agricultural scenarios. By presenting a detailed review from a control-centric perspective, this study aims to serve as a valuable reference for researchers and practitioners developing intelligent agricultural vehicle systems.

Autonomous robots operating in unstructured agricultural environments are often subjected to unmodeled dynamics and time-varying external disturbances caused by uneven and slippery terrain. To address these challenges, this article proposes a novel high-order disturbance observer-enhanced explicit nonlinear model predictive control (HODO-ENMPC) scheme for robust trajectory tracking of agricultural robots. Unlike most existing composite MPC frameworks, the proposed approach directly incorporates disturbance estimates and their predicted dynamics into the predictive model, thereby improving prediction accuracy and enabling offset-free control under both matched and unmatched disturbances. An extended kinematic model is developed to explicitly account for these uncertainties and disturbances. Based on this model, the analytical solution of the ENMPC is derived by a Taylor series expansion of the tracking error up to a specified order. Subsequently, a nonlinear HODO is designed to estimate these disturbances and their derivatives in real-time. Due to the distinct design philosophy, online optimization is not required in the proposed scheme, allowing for high-bandwidth control of fast robot dynamics. The stability of the composite controller is rigorously established. Extensive simulations and field experiments on an agricultural robot validate the effectiveness of the method, demonstrating superior tracking accuracy and enhanced disturbance rejection compared with conventional NMPC, DO-NMPC, and DO-based ENMPC strategies.

This article proposes an optimal trajectory tracking method for a novel robotic platform featuring four-wheel independent driving and on-axle steering (4WID-OAS). Unlike conventional mobile robots, the 4WID-OAS robotic vehicle adopts a unique coordinated differential steering system that possesses the low torque characteristics of differential steering and the high maneuverability of four-wheel independently steering (4WIS). To enable autonomous trajectory tracking, a lightweight control scheme is developed by integrating a four-wheel speed distribution strategy with an optimal tracking control law. The maneuvering model of the 4WID-OAS robot is first investigated as the foundation for the trajectory tracking system. Subsequently, an explicit nonlinear model predictive control (ENMPC) strategy is designed to solve the optimal tracking problem. With the closed-form ENMPC solution, online optimization is not required, thereby enabling high control bandwidth of advanced control strategies with reduced cost. The stability of the system is rigorously proven. Comprehensive simulations and field experiments validate the superiority of the 4WID-OAS robot and demonstrate that the proposed controller effectively balances tracking accuracy with computational efficiency, facilitating real-time deployment on embedded platforms.