The peg-in-hole assembly is an essential robotic task that is widely involved in the industry and daily life. Hole search is a crucial step in peg-in-hole since it aligns the peg and hole where the positional uncertainty is presented. This study introduces a hole search strategy by computing an exploration distribution based on uncertainty analysis. Our method firstly utilizes vision to estimate the hole’s pose. It then performs an uncertainty analysis based on vision estimation and systematic configures to compute the exploration distribution of the hole search. The computed exploration distribution calculates a search trajectory through the distribution and finds the hole. The effectiveness of the search strategy is experimentally verified using a robot manipulator in the peg-in-hole of the optical transceiver, which is a challenging task that has not yet been addressed. The results confirm that the proposed search strategy can achieve a 90 $\%$ success rate on the hole search. Through the rational design of the recovery strategy, our method can secure a 100 $\%$ success rate on the robotic peg-in-hole of apparatus with a small clearance (0.1 mm).

Soft pneumatic actuators (SPAs) are intrinsically light-weight, compliant and therefore ideal to directly interact with humans and be implemented into wearable robotic devices. However, they also pose new challenges in describing and sensing their continuous deformation. In this paper, we propose a hybrid actuator design with bio-inspirations from the lobsters, which can generate reconfigurable bending movements through the internal soft chamber interacting with the external rigid shells. This design with joint and link structures enables us to exactly track its bending configurations that previously posed a significant challenge to soft robots. Analytic models are developed to illustrate the soft-rigid interaction mechanism with experimental validation. A robotic glove using hybrid actuators to assist grasping is assembled to illustrate their potentials in safe human-robot interactions. Considering all the design merits, our work presents a practical approach to the design of next-generation robots capable of achieving both good accuracy and compliance.

Ideally, robots may be designed to adapt to different tasks such as heavy lifting and handling delicate objects, in which the requirements in force compliance and position accuracy vary dramatically. While conventional rigid actuators are usually characterized by high precision and large force output, soft actuators are designed to be more compliant and flexible. In this paper, a lobster-inspired bending module with compliant actuation, enhanced torque output, and reconfigurability in assembling is presented. It is also capable of accurate control of its angular position with variable stiffness. Inspired by the anatomic structure of the lobster leg joint, the bending module has antagonistic soft chambers for actuation and rigid shells for structural protection and support. Theoretical models have been developed and their capability of independently adjusting both the bending angle and stiffness has been evaluated through experiments. A control strategy is constructed to realize angle control and stiffness adaptation. In order to demonstrate various applications of the proposed bending module, reconfigurable robotic fingers are assembled and shown to be capable of generating different motion profiles. In addition, robotic grippers are built for lifting both delicate and heavy objects, demonstrating applications that require both high force and compliant handling.

Citrus rind color is a good indicator of fruit development, and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest schedules. This work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity. A total of 107 sample Navel oranges were observed during the color transformation period, resulting in a dataset containing 7,535 citrus images. A framework is proposed that integrates visual saliency into deep learning, and it consists of a segmentation network, a deep mask-guided generative network, and a loss network with manually designed loss functions. Moreover, the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals, thus effectively shrinking the number of model parameters. The semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694, and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710, which indicate both high quality and similarity of the generated images and are also consistent with human perception. To ease the applications in the real world, the model is ported to an Android-based application for mobile devices. The methods can be readily expanded to other fruit crops with a color transformation period. The dataset and the source code are publicly available at GitHub.