Fluid-Dynamic and Structural Optimization of a Suction-Enabled Autonomous Grass-Cutter Robot

摘要

• Multi-physics optimization of a suction-enabled autonomous grass-cutter robot. • Structural FEA and duct-level CFD inform chassis and suction system design. • Reynolds number, pressure drop, and turbulence intensity analytically validated. • Simulation-integrated workflow including Python, Webots, and Fusion 360. • Suction system energy efficiency and runtime analyzed for battery operation. Autonomous grass-cutter robots are increasingly important for precision agriculture and turf management, offering the potential to reduce labour costs, improve safety, and enhance operational efficiency. However, existing design studies typically address individual subsystems in isolation, lacking a unified framework for comparative evaluation of multi-wheel configurations. To fill this gap, this work introduces a novel, multi-domain integration framework combining structural finite-element analysis (FEA), computational fluid dynamics (CFD) with analytical ΔP–Q and Reynolds number modelling, URDF-based Webots simulation, and Python-driven parametric studies, a unified approach not found in prior grass-cutter robot studies. Key highlights of the paper include: structural optimization, an aluminium 6061-T6 backbone with acrylic panels delivers a 15 % mass reduction while maintaining a safety factor ≥ 2.0 under peak loads; suction performance, comparative CFD and Darcy–Weisbach analyses of duct geometries identify the S-type as optimal, with a validated pressure drop of ∼0.85 kPa and turbulent intensity ∼3.8 % promoting effective debris entrainment; mobility assessment, Webots simulations reveal that a six-wheel chassis enhances traction by 18 % but incurs 12 % higher rolling resistance relative to a four-wheel variant. Analytical modelling modules estimate grass-cutting power, battery endurance (with Peukert’s correction), and terrain sensitivity, enabling rapid design optimization. The inclusion of both simulation and fluid-theoretic validation, including Reynolds number, Darcy–Weisbach analysis, and turbulence intensity estimation, offers a robust methodology for optimizing suction flow performance. This integration not only strengthens mechanical and aerodynamic validation but also supports the sustainable development of closed-loop, compost-capable autonomous grass-cutting platforms.