Optimization design of the modular configuration for apple picking manipulator

Apple harvesting is one of the most complex and least mechanized processes at present. A picking robot can greatly contribute to the advancement of the apple industry. Among them, the picking manipulator is one of the key components in the picking robot. However, the current apple picking manipulators are limited to the complex structures and low modularity, unsuitable for the multi-arm picking operations. It is necessary to develop an apple-picking manipulator with a larger range of motion, high modularity, and a lightweight structure. Efficient, stable, and flexible operation can often be required to optimize the configuration parameters of the manipulators. Particularly, the space constraints rather than motion performance have been focused primarily on optimization in recent years. In this study, a modular configuration of the apple-picking manipulator was designed to optimize the parameters of the motion performance. Firstly, an apple-picking manipulator was designed to fully meet the operational requirements, according to the distribution of the fruits in orchards. The manipulator consisted of three translational and three rotational joints. Specifically, the three translational joints were used to control the motion along the x, y, and z axes, while the three rotational joints were used to control the rotation along the roll, pitch, and yaw axes. The horizontal and telescoping joints were used to realize the different types of motion in the xoy plane using the joint drive motors 1 and 2 with the translational motion. Secondly, the multiple indices were combined with a single objective function in order to evaluate the operational accessibility, structural compactness, velocity, and load smoothness. The analytic hierarchy was employed to determine the weights of each index. The linear weighting was used to generate the objective function. As such, an optimization algorithm was then proposed using an improved hippopotamus optimization algorithm (IHOA). Among them, the hippopotamus optimization (HO) was employed for the global search in the initial stage, the particle swarm optimization (PSO) was to accelerate the convergence using collaboration and learning within the population, and the incorporated simulated annealing (SA) was to introduce the random perturbations. Finally, the simulation and field experiments were performed to validate the operational reachability, structural compactness, velocity, and load stability of the apple picking manipulator. The simulation results showed that the link lengths of the pitch joint, horizontal joint, telescoping joint, and end-effector revolute joint were 122.02, 138.00, 101.45, and 103.12 mm, respectively. The link offsets of the telescoping joint, end-effector revolute joint, end-effector prismatic joint, and twisting joint were 855.00, 166.67, 189.95, and 126.63 mm, respectively. The installation heights of the lower and the upper picking manipulator were 1344.59 and 2460.00 mm, respectively. The experimental results showed that operational accessibility index F₁, structural compactness index F₂, global velocity fluctuation performance index F₃, and global load fluctuation performance index F₄ were 97.05%, 2 882.74 mm, 0.20 m/s, and 0.15 N·m, respectively. Field experiments showed that the maximum absolute torque increments for the pitch joint, joint motor 1, and 2 with translational motion, and the end rotation joint were 0.51, 0.87, 0.80, and 0.79 N·m, respectively, during picking apples at the boundary points. The maximum absolute velocity increments were 0.03, 0.17, 0.17, and 0.01 m/s, respectively. Therefore, the manipulator demonstrated full accessibility to the boundary positions within an operational range of 890.25 to 1 035.47 mm from the tree trunk. This finding can also provide valuable insights to design the modular picking manipulators for apple harvesting.