High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators, have to compromise bandwidth to improve compliance (i.e., backdrivability). In this article, we describe the design and human-robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This article aims to corroborate the underlying philosophy of “design for control,” namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This article demonstrates performance improvement compared with state-of-the-art exoskeletons.

Global berry production and consumption have significantly increased in recent years, coinciding with increased consumer awareness of the health-promoting benefits of berries. Of those consumed, fresh-market blackberries are primarily harvested by hand to maintain postharvest quality. However, the forces applied during hand harvesting can result in major losses of marketable berries to red drupelet reversion (RDR). What is more, manual harvesting is a costly endeavor that accounts for up to 50% of the person-hours involved in berry production. Herein, we present a novel, tendon-driven soft robotic gripper with active contact force feedback control, which leverages the passive compliance of the gripper to allow the gentle harvesting of berries. The versatile gripper can generate a desired force as low as 0.5 N with a mean error of 0.046 N. Field test results indicate that the robotic gripper is capable of harvesting berries with 16% RDR while maintaining a harvesting reliability of 95.24% at a harvesting rate of approximately 4.8 seconds per berry.

Soft robots offer an alternative approach to manipulate within a constrained space while maintaining a safe interaction with the external environment. Owing to its adaptable compliance characteristic, external contact force can easily deform the robot shapes and lead to undesired robot kinematic and dynamic properties. Accurate contact detection and contact location estimation are of critical importance for soft robot modeling, control, trajectory planning, and eventually affect the success of task completion. In this article, we focus on the investigation of a one degree of freedom (1-DoF) soft pneumatic bending robot, which is regarded as one of the fundamental components to construct complex, multi-DoFs soft robots. This 1-DoF soft robot is modeled through the integral representation of the spatial curve, where direct and instantaneous kinematics are calculated explicitly through a modal method. The fixed centrode deviation method is used to detect the external contact and estimate the contact location. Simulation results and experimental studies indicate that the contact location can be accurately estimated by solving a nonlinear least-square optimization problem. Experimental validation shows that the proposed algorithm is able to successfully estimate the contact location with the estimation error of 1.46 mm.

MRI-conditional robotic platforms have proved to be an effective approach for image-guided interventions. In this study, a computer-assisted, pneumatically actuated robot is designed, built, and tested for MRI-guided prostate cancer focal laser ablation (FLA). The robotic manipulator provides two active planar degrees of freedom (DoFs) by using a customized CoreXY frame and one passive rotational DoF. A remote insertion mechanism improves the surgical workflow by keeping the patients inside the scanner during needle insertion. The robotic manipulator is tested in a 3T MR scanner to evaluate its MR compliance, and the results demonstrated that the signal-to-noise ratio (SNR) variation is less than 8%. The in-scanner template positioning accuracy test demonstrates that the manipulator achieves high targeting accuracy with a mean error of 0.46 mm and a standard deviation of 0.25 mm. Phantom studies have shown that the needle insertion accuracy of the manipulator is within 2 mm (mean = 1.7 mm, standard deviation = 0.2 mm).

In this article, the control problem of one segment pneumatically actuated soft robotic arm is investigated in detail. To date, extensive prior work has been done in soft robotics kinematics and dynamics modeling. Proper controller designs can complement the modeling work since they are able to compensate the effects that have not been considered in the modeling, such as the model uncertainties, system parameter identification error, hysteresis, external forces, disturbances, etc. In this letter, we explored different control approaches (kinematic control, PD+feedback linearization, passivity control, adaptive passivity control) and summarized the advantages and disadvantages of each controller. We further investigated the robot control problem in the practical scenarios when the sensor noise exists, actuator velocity measurement is not available, the hysteresis effect is non-neglectable, as well as the existence of external force. Our simulation results indicated that the adaptive passivity control with sigma modification terms, along with a high-gain observer presents a better performance in comparison with other approaches. Although this paper mainly presented the simulation results of various controllers, the work here will pave the way for practical implementation of soft robot control.