Payload capacity is a crucial factor for climbing robots, as it directly affects their ability to carry and transport heavy loads during various climbing tasks. However, many dry adhesion-based legged robots prioritize foot design from a bionic perspective to accomplish various climbing tasks while overlooking payload capacity. To address this issue, a novel adhesion foot structure utilizing micro-suction tape (MST) and a detachment mechanism has been proposed. This design was implemented in the construction and experimental testing of the quadrupedal climbing robot MST-Q with a high payload carrying capacity of up to 2.8 kg for vertical climbing and 0.5 kg for inverted climbing. The robot also demonstrated the ability to traverse obstacles with a payload in the inverted climbing tasks.

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.

The shape memory alloy (SMA)-based robotic hand has been a new emerging technology with potential applications ranging from life service to surgical treatment, because of the characteristics of SMA, such as high power-to-weight ratio, small volume and low driving voltage. However, due to the complex dynamic model and nonlinear aspects of SMA, it is complicated to control an SMA-based robotic hand. This paper presents a novel model free adaptive control for the SMA-based robotic hand system. By applying the Taylor series expansion method and the differential mean value theorem, the SMA based robotic hand system can be transformed into an equivalent linearization model, which merely depends on measurement data without any information on the system. Combined with prescribed performance control, the novel control method can constrain the tracking error in a preassigned domain. Experiments are conducted on the SMA-based robotic hand system to verify the performance of the presented control method.

Effective adaptive grasping capability is regarded as crucial for climbing robots. However, many dry adhesion legged climbing robots are primarily focused on mobility and load capacity to perform various climbing tasks, often overlooking their operational grasping abilities. Furthermore, flexible grippers designed for adaptive grasping are typically not capable of supporting autonomous climbing or perching motions; they must be rigidly integrated with legged climbing robots, which results in increased weight and reduced load capacity. To address this challenge, a novel dry adhesion climbing robot, MST-G, is proposed, featuring autonomous climbing, perching, and flexible adaptive grasping capabilities. During operation, MST-G is integrated with a legged climbing robot to perform tasks, but can autonomously climb when no task is present, thereby reducing load and ensuring stable motion. Additionally, a robust controller based on prescribed performance is introduced and tested on MST-G, which limits the joint tracking error to a prescribed safety limit, ensuring that motion trajectories can be executed safely and reliably.