Design and Optimization of an Adaptive Knee Joint Orthosis for Biomimetic Motion Rehabilitation Assistance

In this paper, an adaptive knee joint orthosis with a variable rotation center for biomimetic motion rehabilitation assistance suitable for patients with knee joint movement dysfunction is designed. Based on the kinematic information of knee joint motion obtained by a motion capture system, a Revolute-Prismatic-Revolute (RPR) model is established to simulate the biomimetic motion of the knee joint, then a corresponding implementation for repetitively driving the flexion-extension motion of the knee joint, mainly assembled by a double-cam meshing mechanism, is designed. The pitch curve of each cam is calculated based on the screw theory. During the design process, size optimization is used to reduce the weight of the equipment, resulting in a reduction from 1.96 kg to 1.16 kg, achieving the goal of lightweight equipment. Finally, a prototype of the designed orthosis with the desired biomimetic rotation function is prepared and verified. The result shows that the rotation center of the prototype can achieve biomimetic motion coincident with the rotation center of an active knee joint, which can successfully provide rehabilitation assistance for the knee joint flexion-extension motion.

Target-Following Control of a Biomimetic Autonomous System Based on Predictive Reinforcement Learning

Biological fish often swim in a schooling manner, the mechanism of which comes from the fact that these schooling movements can improve the fishes' hydrodynamic efficiency. Inspired by this phenomenon, a target-following control framework for a biomimetic autonomous system is proposed in this paper. Firstly, a following motion model is established based on the mechanism of fish schooling swimming, in which the follower robotic fish keeps a certain distance and orientation from the leader robotic fish. Second, by incorporating a predictive concept into reinforcement learning, a predictive deep deterministic policy gradient-following controller is provided with the normalized state space, action space, reward, and prediction design. It can avoid overshoot to a certain extent. A nonlinear model predictive controller is designed and can be selected for the follower robotic fish, together with the predictive reinforcement learning. Finally, extensive simulations are conducted, including the fix point and dynamic target following for single robotic fish, as well as cooperative following with the leader robotic fish. The obtained results indicate the effectiveness of the proposed methods, providing a valuable sight for the cooperative control of underwater robots to explore the ocean.

Precurved, Fiber-Reinforced Actuators Enable Pneumatically Efficient Replication of Complex Biological Motions

Much of the research on bioinspired soft robotics has focused on capturing the interplay of biological form and function. However, existing soft robotic actuators are mostly made with linear or planar fabrication orientations that do not represent the resting geometry of complex biological systems, such as curved musculature. This work introduces the ability to create fiber-reinforced actuators with precurved configurations. By tuning variables such as dimensions and fiber angles, an optimization algorithm can prescribe the mechanical fabrication parameters to create a fiber-reinforced actuator that can generate controlled motion to follow a desired input trajectory. Precurved configurations introduce an additional optimization parameter, the initial bend angle, allowing for a more accurate and robust algorithm and generating a median percent error of <1%. With a customized software tool, we can take existing motion data from biological systems-such as medical imaging-and build soft robotic actuators optimized to replicate these trajectories. We can predict the motion of precurved actuators both analytically and numerically and replicate the motion experimentally, with excellent trajectory matching between the three. In constructing actuators that better match the native forms found within biological systems, we find that precurved actuators are more efficient than their initially straight counterparts. This pneumatic efficiency allows for the use of control systems with lower power and precision, lowering the economic cost of the associated control hardware, while more accurately replicating the biological motion. Taking two examples from biology, that of the human diaphragm during respiration and that of a jellyfish bell during locomotion, we design and generate fiber reinforced actuators to mimic these motions.

Light-Driven Self-Oscillating Actuators with Phototactic Locomotion Based on Black Phosphorus Heterostructure

Developing self-oscillating soft actuators that enable autonomous, continuous, and directional locomotion is significant in biomimetic soft robotics fields, but remains great challenging. Here, an untethered soft photoactuators based on covalently-bridged black phosphorus-carbon nanotubes heterostructure with self-oscillation and phototactic locomotion under constant light irradiation is designed. Owing to the good photothermal effect of black phosphorus heterostructure and thermal deformation of the actuator components, the new actuator assembled by heterostructured black phosphorus, polymer and paper produces light-driven reversible deformation with fast and large response. By using this actuator as mechanical power and designing a robot configuration with self-feedback loop to generate self-oscillation, an inchworm-like actuator that can crawl autonomously towards the light source is constructed. Moreover, due to the anisotropy and tailorability of the actuator, an artificial crab robot that can simulate the sideways locomotion of crabs and simultaneously change color under light irradiation is also realized.

A bioinspired soft actuated material

A class of soft actuated materials that can achieve lifelike motion is presented. By embedding pneumatic actuators in a soft material inspired by a biological muscle fibril architecture, and developing a simple finite element simulation of the same, tunable biomimetic motion can be achieved with fully soft structures, exemplified here by an active left ventricle simulator.