Abstract:

[Significance] Multi-machine collaboration of intelligent agricultural machinery refers to a coordinated field-operation mode in which multiple agricultural machines jointly perform agricultural production tasks through task allocation, information sharing, and cooperative control. It involves multiple technical aspects, including system architecture, sensing and localization, communication, decision-making and planning, dynamic interaction, and cooperative control. The technical boundaries between multi-machine collaboration, single-machine autonomous operation, and coordination within tractor-implement units must also be clearly delineated. This review summarizes the research progress, key technologies, and typical applications of multi-machine collaborative operation for intelligent agricultural machinery, aiming to provide a reference for subsequent research and engineering implementation. [Progress] Multi-machine collaboration is defined as an agricultural operation system comprising two or more machines with relatively independent mobility or operating capabilities that share inter-machine information and establish coupling in task execution, path planning, motion control, or operation timing. Single-machine autonomous navigation and internal coordination within tractor-implement units are regarded only as supporting technologies for multi-machine collaboration. This review first analyzes multi-machine system architectures and compares the characteristics and applicable conditions of centralized, distributed, and hybrid architectures. Centralized architectures emphasize global information aggregation, unified task allocation, and coordinated path planning, whereas distributed architectures rely more on local sensing, peer-to-peer information exchange, and decentralized decision-making. Hybrid architectures combine global coordination with local autonomy and are more suitable for complex operating conditions that require both overall scheduling and rapid local responses. The key enabling technologies are then reviewed. In multi-source perception and cooperative localization, research has gradually expanded from single-machine multi-sensor fusion to inter-machine relative localization, state sharing, cooperative perception, and shared map construction. In communication, the controller area network (CAN) and the ISO 11783-based agricultural machinery communication protocol (ISOBUS) provide a foundation for intra-machine data exchange, while Wi-Fi, LoRa, 4G/5G, and radio links are commonly used for inter-machine communication. In decision-making and planning, research has progressed from static task allocation and independent path generation toward dynamic task reallocation, task-path joint optimization, spatiotemporal conflict resolution, and global-local collaborative planning. In dynamic modeling and interaction, wheel-soil slip, sideslip, tractor-implement articulation, trajectory deviation, and relative motion among machines are increasingly incorporated into planning and control loops to improve trajectory feasibility and dynamic coordination. In formation control, virtual-structure, behavior-based, and leader-follower approaches have been widely investigated, with growing attention to dynamic obstacle avoidance, disturbance rejection, communication constraints, and safety control. Typical application scenarios are further summarized from the perspectives of homogeneous and heterogeneous systems. Homogeneous systems primarily improve parallel operational capacity through regional decomposition, task allocation, and coordinated coverage, whereas heterogeneous systems emphasize functional complementarity among machines with different sensing, operating, and transport capabilities. Representative applications include multi-machine harvesting, harvester-transport coordination, seeding, spraying, air-ground cooperation, and harvesting-transfer operations. These comparisons reveal differences in coordination requirements, information flows, and control relationships across representative agricultural operation scenarios. [Conclusions and Prospects] Several challenges remain, including perception degradation in complex field environments, communication delays and interruptions, asynchronous multi-source information, task and path conflicts, dynamic disturbances, equipment failures, insufficient interoperability, and limited large-scale field validation. Future research should focus on reconfigurable collaborative architectures, heterogeneous communication networks, multi-machine cooperative perception and shared mapping, dynamic task and path replanning, fault-tolerant formation control, embodied cooperative control, digital twins, and integrated simulation-field evaluation. Long-term, cross-field, and cross-season experiments are also needed to verify system reliability, scalability, safety, and engineering applicability, thereby supporting the transition of intelligent agricultural machinery from single-machine autonomy to continuous, robust, and large-scale multi-machine collaboration.

Key words: multi-machine collaboration, perception and communication, task allocation, path planning, dynamics modeling and interaction, formation control, swarm operation