<p dir="ltr">The research presents the structure and a prototype an innovative parallel robotic structure using 3 mobile bases for actuation and hybrid motion. A system identification was performed to verify the model of the robot.</p>

<p>Robot grasping is emerging as an active area of research in robotics as the interest in human-robot interaction is gaining worldwide because of diverse industrial settings for sharing tasks and workplaces. It mainly focuses on the quality of generated grasps for object manipulation. However, despite advancements, these methods need to consider the human-robot collaboration settings where robots and humans will have to grasp the same objects concurrently. Therefore, generating robot grasps compatible with human preferences of simultaneously holding an object is necessary to ensure a safe and natural collaboration experience. In this work, we propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-the-art robot grasping methods through simulated and real-robot experiments and user studies. In real robot experiments, our method achieves about 88% success rate in producing stable grasps that allow humans to interact and grasp objects simultaneously in a socially compliant manner. Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.</p> <p>To facilitate the grasping process, we also introduce a vision-language model that works as a pre-processing system before the grasping action takes place. In most settings, the robots are equipped with sensors that allow them to capture the scene, on which the vision model is used to do a detection task and objectify the visible contents in the environment. The language model is used to program the robot to make it possible for them to understand and execute the required sequence of tasks. Using the process of object detection, we build a set of object queries from the sensor image and allow the user to provide an input query for a task to be performed. We then perform a similarity score among these queries to localize the object that needs attention, and once identified, we can use a grasping process for the task at hand.</p>

<p dir="ltr">This paper aims to gain a deep understanding of up-to-date research and development on modular self-reconfigurable robots (MSRs) through a thorough survey of market demands and published works on <i>design methodologies</i>, <i>system integration</i>, <i>advanced controls</i>, and <i>new applications</i>. Some limitations of existing mobile MSR are discussed from the reconfigurability perspective of mechanical structures, and a novel MSR system is proposed to address the identified limitations of existing MSRs. The comprehensive set of <i>Functional Requirements</i> (FRs) of MSRs is discussed, from which the mechanical designs of MSR were created, and the system was prototyped and built for testing. Three main innovations of the designed modules for MSR are to (1) share torque power, (2) customize the size for a given task, and (3) have a low number of actuated motors while still maintain a motion with high <i>Degrees of Freedom</i> (DoF) to overcome the constraints by the power capacities of individual motors; this helps to increase reconfigurability, reduce cost, and reduce the size of conventional MSRs.</p>

<p dir="ltr">Over 150,000 people undergo lower-extremity amputations yearly in the United States. In recent years, multiple efforts have been made to improve the human-robot interaction between amputees and active lower limb prostheses. Using lightweight wearable technologies has been a viable solution to implement algorithms that can estimate gait kinematics and prosthesis users’ intent. Examples of wearable technologies include inertial measurement units, strain gauges, and electromyography sensors. Kinematic and force data is inputted into an Error-State Kalman filter to estimate the inversion-eversion, external-internal, and dorsiflexion-plantarflexion ankle angle. The filter tracked the ankle angle with an accuracy of 0.7724°, 0.8826°, and 1.3520°, respectively. The gait phase was estimated using a linear regression model based on a shank kinematics ground truth pattern with an average normalized accuracy of 97.79 %. A numerical simulation of a gait emulator in the form of a 3-Revolute-Prismatic-Revolute (3-RPR) manipulator. The gait emulator can test lower limb prostheses independent of human subjects, eliminating many hurdles associated with human subject testing. The manipulator was simulated with two control strategies: a traditional PID and a hybrid PID + Active Force Control controller (AFC). The hybrid PID+AFC provided higher accuracy in tracking the desired end-effector trajectory due to improved disturbance rejection. A low-cost surface electromyography (sEMG) platform was developed to robustly acquire sEMG signals, with an overall component cost of 35.06 US$. The sEMG platform integrates directly into a Micro:bit microcontroller through an expansion board. During testing with human subjects, sEMG Micro:bit platform had a reported average signal-to-noise ratio of 24.7 dB.</p>