This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Existing multi-agent path-finding (MAPF) methods either assume homogeneous robots or require inter-robot communication, limiting their field deployment in large orchards. We introduce conflict-based search for heterogeneous tasks (CBS-HT), a fixed-priority path-planner tailored for unmanned ground vehicles (UGVs) that perform monitoring, harvesting, and transportation. By design, CBS-HT relaxes strict completeness and optimality guarantees in exchange for practical real-time performance on large robot teams. CBS-HT integrates priority-based resolution into a CBS-inspired search framework, resolving path clashes without on-line negotiation and thus avoiding radio congestion. Three evaluation tiers—simulation, lab-scale, and orchard—test scalability, mission-level efficiency, and real-world robustness. Compared with an uncoordinated baseline, CBS-HT shortens high-priority robots’ travel distance by up to 19.1 m and mission time by 28.4 s, while maintaining a 100% collision-free success rate for tested instances. These gains demonstrate that fast, priority-aware planning can enable practical, communication-free coordination of heterogeneous UGV fleets in commercial orchards.