Faced with complex and unstructured construction environments, wheeled and tracked robots exhibit significant limitations in terrain adaptability and flexibility, making it difficult to meet the requirements of autonomous operation. Inspired by ants in nature, this paper proposes a leg configuration design and optimization method tailored for construction scenarios, aiming to enhance the autonomous mobility of construction robots. This paper analyzes the full operational motion performance of the leg during both swing and stance phases. First, based on kinematic modeling and multi-dimensional workspace analysis, the concept of an "improved workspace" is introduced, and graphical methods are used to optimize the leg dimensions during the swing phase. Furthermore, a new concept of "average manipulability" is introduced based on the velocity Jacobian matrix, and numerical solutions are applied to obtain the leg segment ratio that maximizes manipulability. To overcome the difficulties associated with traditional analytical methods, virtual prototype simulations are conducted in ADAMS to explore the relationship between the robot body's optimal flexibility and leg segment proportions. In summary, the leg segment proportions with the best comprehensive motion performance are obtained. This study presents the first multi-dimensional quantitative evaluation framework for leg motion performance tailored for construction environments, providing a structural design foundation for legged construction robots to achieve autonomous mobility in complex terrains.

Scombrid fishes and tuna are efficient swimmers capable of maximizing performance to escape predators and save energy during long journeys. A key aspect in achieving these goals is the flexibility of the tail, which the fish optimizes during swimming. Though, the robotic counterparts, although highly efficient, have partially investigated the importance of flexibility. We have designed and tested a fish-like robotic platform (of 30 cm in length) to quantify performance with a tail made flexible through a torsional spring placed at the peduncle. Body kinematics, forces, and power have been measured and compared with real fish. The platform can vary its frequency between 1 and 3 Hz, reaching self-propulsion conditions with speed over 1 BL/s and Strouhal number in the optimal range. We show that changing the frequency of the robot can influence the thrust and power achieved by the fish-like robot. Furthermore, by using appropriately tuned stiffness, the robot deforms in accordance with the travelling wave mechanism, which has been revealed to be the actual motion of real fish. These findings demonstrate the potential of tuning the stiffness in fish swimming and offer a basis for investigating fish-like flexibility in bio-inspired underwater vehicles.

The paper presents the kinematic modelling for the coupled motion of a 6-DOF surgical parallel robot PARA-SILSROB which guides a mobile platform carrying the surgical instruments, and the actuators of the sub-modules which hold these tools. To increase the surgical procedure safety, a closed form solution for the kinematic model is derived and then, the forward and inverse kinematic models for the mobile orientation platform are obtained. The kinematic models are used in numerical simulations for the reorientation of the endoscopic camera, which imposes an automated compensatory motion from the active instruments' mod-ules.

Attempt to use convolutional neural network to achieve kinematic analysis of plane bar structure. Through 3dsMax animation software and OpenCV module, self-build image dataset of geometrically stable system and geometrically unstable system. we construct and train convolutional neural network model based on the TensorFlow and Keras deep learning platform framework. The model achieves 100% accuracy on the training set, validation set, and test set. The accuracy on the additional test set is 93.7%, indicating that convolutional neural network can learn and master the relevant knowledge of kinematic analysis of structural mechanics. In the future, the generalization ability of the model can be improved through the diversity of dataset, which has the potential to surpass human experts for complex structures. Convolutional neural network has certain practical value in the field of kinematic analysis of structural mechanics. Using visualization technology, we reveal how convolutional neural network learns and recognizes structural features. Using pre-trained VGG16 model for feature extraction and fine-tuning, we found that the generalization ability is inferior to the self-built model.

Kinematics analysis is a crucial part of multiple joint-enabled robots. A multi-joint enabled robot requires extensive mathematical calculations to be done so the end effector's position can be determined with respect to the other connective joints involved and their respective frames in a specific coordinate system. For a locomotive quadruped robot, it is essential to determine two types of kinematics for the robot's leg position on the coordinate. For the part of forward kinematics, it measures the position, and joint angles can be calculated using inverse kinematics. Mathematical derivation of the joint angles is derived here, and Python-based simulation has been done to verify and simulate the robot's locomotion. This approach has been tested beneficial over other methods as Python-based code is used which makes it easier to do serial communication and therefore it could be deployed in a micro-controller unit to interact with a prototype leg.

Designing a mechanism to mimic the motion of a common house gecko is the objective of this work. The body of the robot is designed using four five-bar mechanisms (2-RRRRR and 2-RRPRR) and the leg is designed using four four-bar mechanisms. The 2-RRRRR five-bar mechanisms form the head and tail of the robotic lizard. The 2-RRPRR five-bar mechanisms form the left and right sides of the body in the robotic lizard. The four five-bar mechanisms are actuated by only four rotary actuators. Of these, two actuators control the head movements and the other two control the tail movements. The RRPRR five-bar mechanism is controlled by one actuator from the head five-bar mechanism and the other by the tail five-bar mechanism. A tension spring connects each active link to a link in the four bar mechanism. When the robot is actuated, the head, tail and the body moves, and simultaneously each leg moves accordingly. This kind of actuation where the motion transfer occurs from body of the robot to the leg is the novelty in our design. The dimensional synthesis of the robotic lizard is done and presented. Then the forward and inverse kinematics of the mechanism, and configuration space singularities identification for the robot are presented. The gait exhibited by the gecko is studied and then simulated. A computer aided design of the robotic lizard is created and a prototype is made by 3D printing the parts. The prototype is controlled using Arduino UNO as a micro-controller. The experimental results are finally presented based on the gait analysis that was done earlier. The forward walking, and turning motion are done and snapshots are presented.

This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.