Wesley Au

In the dynamic and unstructured environment where horticultural crops grow, obstacles and interference frequently occur but are rarely addressed, which poses significant challenges for robotic harvesting. This work proposed a tactile-enabled robotic grasping method that combines deep learning, tactile sensing, and soft robots. By integrating fin-ray fingers with embedded tactile sensing arrays and customized perception algorithms, the robot gains the ability to sense and handle branch interference during the harvesting process and thus reduce potential mechanical fruit damage. Through experimental validations, an overall 83.3–87.0% grasping status detection success rate, and a promising interference handling method have been demonstrated. The proposed grasping method can also be extended to broader robotic grasping applications wherever undesirable foreign object intrusion needs to be addressed.

Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.

Compared to traditional rigid robotics, soft robotics has attracted increasing attention due to its advantages as compliance, safety, and low cost. As an essential part of soft robotics, the soft robotic gripper also shows its superior while grasping the objects with irregular shapes. Recent research has been conducted to improve its grasping performance by adjusting the variable effective length (VEL). However, the VEL achieved by multi-chamber design or tunable stiffness shape memory material requires complex pneumatic circuit design or a time-consuming phase-changing process. This work proposes a fold-based soft robotic actuator made from 3D printed filament, NinjaFlex. It is experimentally tested and represented by the hyperelastic model. Mathematic and finite element modelling is conducted to study the bending behaviour of the proposed soft actuator. Besides, an antagonistic constraint mechanism is proposed to achieve the VEL, and the experiments demonstrate that better conformity is achieved. Finally, a two-mode gripper is designed and evaluated to demonstrate the advances of VEL on grasping performance.

The aim of this paper is to define a process to expand the overall reachable workspace of the 5R mechanism and to conduct feasible path planning between configurations within that space, even in cases where the initial and final configurations exist in different modes. It is known that if a mechanism has multiple working modes, there is a unique workspace for each configuration, and they are often only partially connected to each other in a complex manner. Serial singularities allow the mechanism to remain stiff and controllable. These regions are utilised to link disconnected workspaces to maximise the overall reachable workspace to form the global workspace road map. This paper focuses on the 2-DOF path planning of the 5R planar mechanism. A generalised method for analysing complex workspaces of parallel mechanisms is proposed, the rotary disk search for finding the connected reachable workspace, a strategy for global path finding by analysing the global workspace road map and local path generation are discussed.