Robotics

Biological muscles generate tension from the combined contribution of the passive elastic recoil and the actively controlled contractile mechanisms. Understanding and replicating these passive and active tensions is necessary and beneficial for designing soft robotic actuators that emulate muscle-like behavior. In the current work, the aim is to develop a mathematical framework for modeling both the passive and active tensions in a biological muscle as functions of muscle length and contraction velocity. We will describe the passive tension by a nonlinear monotonically increasing function of length with threshold behavior in order to capture the experimentally observed stiffening occurring in stretched biological muscles. We will model the active tension using the superposition of Gaussian functions that relate bell-shaped tension-length with a flat plateau over the optimal length of the sarcomere. The parameters of this Gaussian representation of the active tension-length relation are determined from formulating a least-squares optimization problem, such that a Characteristic (indicator) function is approximated globally over the optimal length range of the sarcomere by summation of some Gaussian functions. The closed-form formulations for the required integrals are derived using the integral of the product of two Gaussian functions over ${\mathbb{R}}^n$ as well as the error function which enables efficient parameter identification. We will also propose a symmetric tension–velocity relation that distinguishes three phases of concentric, eccentric and isometric contractions, and is parametrized directly by measurable quantities of isometric tension and maximum shortening velocity. The passive and active tensions are finally combined into a unified comprehensive tension model in which the exponentially modeled passive tension is added up to the active contribution, formulated as the product of the activation level, a normalized length-dependent factor and a normalized velocity-dependent factor. The resulting model reproduces canonical tension-length and tension-velocity relations and provides an analytically tractable comprehensive tension model that can be embedded in the dynamics of soft and continuum robot actuators inspired by biological muscles.

Fly-casting is a throwing technique in which a flexible rod is used to cast a lightweight line. In skilled fly-casting, a phenomenon known as the rebound phenomenon is observed, where the residual vibration of the rod tip is suppressed by the re-acceleration of the rod handle during the rod-stop phase. This vibration suppression plays an essential role in the casting performance; however, an engineering method for this phenomenon has not been established. Therefore, the purpose of this study is to propose a trajectory-shaping method by interpreting the rebound phenomenon as a vibration suppression control problem for flexible systems with nonzero initial conditions. The proposed method applies a conventional shaping framework to rod systems by introducing a second-order approximation and repeatedly shaping the input trajectory to suppress the approximation errors. Through simulations using a rod model, it was shown that the shaped trajectory yields the characteristic re-acceleration of the rod-handle angular velocity during the rod-stop phase, consistent with the rebound phenomenon. Through experiments using a robotic prototype, it was confirmed that the rod tip vibration amplitude is suppressed by over 80% in two types of casting. These results are useful for further studies on the engineering realization of fly-casting.

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft Azure-powered speech recognition processes these commands to extract keywords and then localize requested objects on a predefined building map. Pioneer P3-DX, equipped with a Hokuyo LiDAR, enables autonomous navigation and obstacle avoidance, while Kinect v2, integrated with the YOLOv8 algorithm, facilitates object recognition and localization. The robot retrieves and delivers the user’s requested objects while following the shortest available path. Experimental evaluations in a home environment demonstrate the system’s effectiveness in identifying and retrieving requested objects. The subsystems achieve a success rate of 85–95% across more than 50 runs, highlighting their strong performance. The proposed approach provides a proof of concept for future advancements in assistive robotics, demonstrating the seamless integration of advanced technologies into a cost-effective and user-friendly platform.