Li Tian

Hilly orchards, which account for over 60% of China’s orchard area, present complex terrain that limits the effectiveness of conventional agricultural machinery, resulting in high labor requirements and low operational efficiency. Agricultural robots offer a promising solution, with path planning as a critical enabling technology. However, existing methods often suffer from limited terrain adaptability, inadequate obstacle avoidance, poor dynamic responsiveness, and compromised operational safety. To address these challenges, this study proposes a dual closed-loop global–local path planning framework for orchard robots operating in hilly environments. A terrain-aware traffic cost model and a slip-inclusive kinematic model are first developed to characterize traversability and motion stability. Building on these models, the A* algorithm is enhanced for global path planning, while the Dynamic Window Approach (DWA) is improved to enable local obstacle avoidance while satisfying terrain stability constraints. A double closed-loop control architecture integrates global path constraints with local environmental feedback and real-time replanning, achieving a balance between global optimality and local responsiveness. Field experiments demonstrate that the proposed strategy reduces path length by 2.1–19.8% and slip ratio by 2.7–8.0% compared with conventional methods, achieves a 100% obstacle-avoidance success rate, and maintains lateral trajectory deviations below 15.2 cm. These results validate the method as a safe, stable, and efficient approach for robotic operations in hilly orchards, highlighting its potential for practical deployment in complex agricultural environments.

To explore robotic peach picking in different modes, this study examined the effects of various peach picking modes on harvesting force and time. A finite element model of peach harvesting structure was established, and harvesting experiment parameters were based on the Box–Behnken design. Harvesting was simulated to collect response time and force data. Subsequently, the optimal harvesting rate under different picking modes was determined. Different picking modes were tested by simulating identical fruit harvesting in the laboratory at the optimal harvesting speed to determine the peak harvesting force and duration. The Bend mode had the lowest picking pressure and the shortest average picking time at 0.7 MPa and 4.2 s, respectively. The Pull and Twist modes had similar pressures and picking times at 1.2 and 1.1 MPa and 5.2 and 5.6 s, respectively. Harvesting in the orchard allowed for harvesting force and duration measurement under different picking modes. The differences in picking pressure and time among the three picking modes increased compared with those of simulated picking, with specific patterns being observed. Picking pressure appeared at P1max, and differences in picking time were prevalent during separation. This study offers valuable insights for future improvements in harvesting modes and efficiency enhancement.