Twisted-coiled polymer actuators (TCA) have many interesting properties that show potentials for making high performance bionic devices. This letter presents a long short term memory (LSTM) model to predict the behavior of an antagonistic joint driven by hybrid TCA bundles made from Spandex and nylon fibers. By using automatic differentiation, which can be done with PyTorch, a linearized discrete state space model can be formulated and then be utilized in model predictive control (MPC). The experimental results show that by combining LSTM and MPC in modeling and control of the TCA, both high prediction performance and control performance can be achieved. It is verified that using the LSTM model, the joint angle and actuator temperatures can be predicted accurately with an avarage steady state error less than 0.1 deg and 0.2 Cel. deg., respectively. The MPC control results show the controller's ability to reach the desired joint angle with an avarage steady state error of 0.21 degrees in set-point regulation and track a sinusoidal waveform at composite frequencies of 0.1 Hz to 0.15 Hz with the steady-state error less than 0.38 degrees while changing joint stiffness.

This letter proposes a variable stiffness robotic hand driven by twisted-coiled polymer actuators (TCAs) connected in series with a servomotor. Unlike slow conventional TCA systems, the grasping motion of the proposed hand is realized by the servomotor, which improves the speed of actuation significantly. It was shown that by using the servomotor, the rise time and settling time in set-point regulation control were $\text{0.1}\;{\mathrm {s}}$ and $\text{0.33}\;{\mathrm {s}}$ , respectively, which were significantly improved compared with those results of 3.26 s and 12.40 s using TCAs only. Since the stiffness of TCAs can be changed by varying their temperature, the stiffness of the hand can be realized by changing the temperature of the TCA. By applying model predictive control (MPC) and active cooling, the temperature of the TCA can be controlled to follow a sinusoidal waveform at a frequency of 0.1 Hz with an average steady-state error of 0.9 Celsius degrees. The proposed robotic hand can grasp a load of 80 g and approximately achieve a 5-fold variable stiffness. In addition, the fingers of the hand with soft joints are fully made using flexible thermoplastic polyurethane (TPU), therefore reducing the need for complex assembly processes.

In this letter, we propose a method of computationally efficient modeling of vacuum suction grippers. The model is better at predicting the load-carrying capability of a vacuum suction cup while being easy to simulate in a virtual environment. The contact geometry between the suction cup and the curved surface of the handling object is analyzed to compute the deformation and the distributed force between them. We break down the deformation of the suction cup into two corresponding forces, compression force and bending force where the former is the deformation of the cup related to axial direction and the latter is modeled by a beam deflection with fixed ends. Our modeling leads to conditions for seal formation. In our experimental tests, the model was able to accurately predict the load-carrying capabilities for suction grasps on simple as well as curved surfaces. The model was also implemented into a robotic simulator, CoppeliaSim, where the deformation and the state of the suction grasp can be simulated in real-time.

This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the proposed optimization algorithm can produce a feasible solution to scheduling the cooking order for each task and for each associated arms so that the total cooking time and the total moving distance for each arm are minimized. We use a subproblem optimization strategy in which the number of tasks to be planned is divided into several groups instead of planning all tasks at the same time. By doing so, the planning time can be significantly decreased, making the algorithm practical for online implementation. The feasibility of our optimization method and the effectiveness of the subproblem optimization strategy were verified through simulated experiments consisting of 30 to 120 tasks. The results showed that our strategy is advantageous in terms of computation time and makespan for large problems.

Twisted-coiled polymer actuators (TCAs) have been known for many interesting properties such as inherent compliance, high stroke, high power density. However, the current TCAs normally come along with traditional sensing methods such as encoders attached to the link, which limits the application of the soft actuator. This letter presents a biologically inspired soft robotic muscle from TCAs with embedded proprioceptive position feedback. The embedded elongation sensor fabricated from EcoFlex and liquid metal has little hysteresis and is soft, which can help detect the muscle’s displacement. Moreover, the position of the muscle module is controlled by applying an adaptive backstepping sliding mode control algorithm which can provide finite-time convergence, high tracking performance with no singularity, and less chattering. It is verified from the experimental results that the muscle can be controlled with an average steady state error of 0.15 mm and follow a sinusoidal waveform with composite frequencies of 0.01 Hz and 0.03 Hz using natural cooling. The proposed controller has better performance over computed torque method control and adaptive sliding mode control in terms of maximum error, mean steady state error, rising time, peak time, and overshoot. A robotic hand application driven by the proposed artificial module was also demonstrated.

Pipelines are embedded in industrial sites and residential environments, and maintaining these pipes is crucial to prevent leakage. Given that most pipelines are buried, the development of robots capable of exploring their interiors is essential. In this work, we introduce a novel in-pipe robot utilizing Continuously Variable Transmission (CVT) mechanisms for navigating various pipes, including vertical and curved pipes. The robot comprises one air motor, three CVT mechanisms, and six wheels at the end of six slider-crank mechanisms, including three active and three idler ones. The slider crank and spring mechanism generate a wall press force through the wheel to prevent slipping inside the pipe. This capability allows the robot to climb vertical pipes and adapt to various pipe diameters. Moreover, by combining CVT mechanisms, whose speed ratios between the driver and driven pulleys are passively adjusted by the position of the slider, the robot achieves independent and continuous speed control for each wheel. This enables it to navigate pipes with various geometries, such as straight–curved–straight pipes, using only one motor. Since active control of each wheel is not needed, the complexities of the robot controller can be significantly reduced. To validate the proposed mechanism, MATLAB simulations were conducted, and in-pipe driving experiments were executed. Both simulation and experimental results have shown that the robot can effectively navigate curved pipes with a maximum speed of 17.5 mm/s and a maximum traction force of 56.84 N.