This paper presents the design, kinematics, and development of a tendon-driven continuum robot for surgical applications. The continuum robot has a flexible and adaptable construction that imitates the movements of natural organisms. The robot’s unique structure comprises disk members, springs, and a continuum backbone member, enabling it to bend, contract, and deform in complex ways. The robot is operated by pulling tendons, giving it the agility and flexibility necessary to bend in confined spaces. This study discusses the main design considerations and challenges in creating a tendon-driven continuum robot, including the kinematics of the four-tendon mechanism. The developed tendon-driven continuum robot is categorized into two modules: the distal end and the proximal end. The distal end consists of the continuum robot structure, whereas the proximal module consists of the actuating unit that actuates the distal end. The experimental results demonstrate the continuum robot’s ability to be used in medical fields and pipe inspections because of the miniaturized design of the distal end, which allows it to enter confined spaces. This paper provides valuable insights into the design, kinematics, and appropriate materials to build a tendon-driven continuum robot; its bending and deformation capabilities can be used in many fields, especially surgical applications and confined space explorations.

The global waste management problems interacting with increased percentages of urbanization and industrialization are discussed within this research. Improved recycling systems are critically studied with deep learning techniques. In total, five models, such as 2-layer CNN, 3-layer CNN, VGG16-Mobile, MobileNet, and MobileNetV2 with self-attention, have been tested on waste images to be classified as organic or recyclable waste. Normalization and augmentation in data preprocessing gained high levels of accuracy on classification, especially with transfer learning models like VGG16 and MobileNet. Importantly, the addition of self-attention layers in MobileNetV2 improves the accuracy above 93% while focusing on significant parts of an image. Therefore, deep learning results in significant outcomes in waste management and minimization of environmental effects.