Abstract: Weed management in densely planted orchards, particularly in hilly and mountainous regions, presents serious challenges due to limited space, obstructive branches, and the inability of conventional mowing equipment to perform intra-row weeding or navigate tight transitions. To address these limitations, this study aimed to develop an intelligent, fully electric-driven weeding robot capable of performing both inter-row and intra-row weeding operations with obstacle avoidance functionality, specifically designed for the spatial and terrain constraints of closed-canopy orchard environments. The robot was designed with a modular hardware and control architecture that integrates four core systems: a dual-motor tracked chassis for enhanced terrain adaptability; an electric push-rod mechanism enabling adjustable cutting height in response to undulating terrain; a torsion-spring-based passive avoidance mechanism for intra-row weeding blades; and an isolated direct current to direct current (DC-DC) converter system ensuring stable and safe power distribution across high- and low-voltage subsystems. To improve the robot’s motion control accuracy under unstructured field conditions, a fuzzy proportional–integral–derivative (PID) controller was implemented for the chassis drive system. To optimize the controller’s parameters, an improved version of the Sparrow Search Algorithm (SSA) was proposed. This optimization algorithm incorporated chaotic population initialization, adaptive dynamic step adjustment, and reverse learning strategies to improve convergence performance and prevent premature local optima. Simulation tests demonstrated that the improved fuzzy PID controller exhibited significantly enhanced tracking performance and robustness. Compared with both standard SSA-tuned fuzzy PID and conventional PID controllers, the proposed control method reduced steady-state error and overshoot when subjected to step inputs, indicating superior response stability and dynamic adaptability. Field experiments were conducted in a closed-canopy hilly orchard under full-load operating conditions to validate the robot’s real-world effectiveness. The robot achieved an average working speed of 0.7811 m/s, an average turning trajectory diameter of 984 mm, and maintained reliable operation on slopes with gradients up to 16.8°. The heading angle deviation remained within ±3° throughout navigation. In terms of agronomic effectiveness, the inter-row weeding rate reached an average of 91.97%, while the obstacle avoidance success rate reached 95.58%, demonstrating the robot’s ability to safely maneuver around tree trunks and irregular obstacles. The stubble height consistency coefficient exceeded 85%, ensuring uniform cutting height, and the cutting width utilization rate surpassed 90%, reflecting high operational efficiency. All evaluated metrics met the original design targets, confirming the system’s technical feasibility and functional robustness. The developed robot successfully addressed the key challenges of maneuverability, terrain adaptability, and precision weeding in hilly, spatially constrained orchard environments. The integration of an optimized fuzzy PID controller and the improved metaheuristic tuning algorithm contributed to enhanced control performance and autonomous decision-making. This research offers valuable theoretical and technical support for future development of electric-driven weeding robots targeting closed-canopy orchards, and contributes to the broader advancement of intelligent orchard machinery and sustainable orchard management systems.