Disassembly Automation

Disassembly Automation

This book presents a number of aspects to be considered in the development of disassembly automation, including the mechanical system, vision system and intelligent planner. The implementation of cognitive robotics increases the flexibility and degree of autonomy of the disassembly system. Disassembly, as a step in the treatment of end-of-life products, can allow the recovery of embodied value left within disposed products, as well as the appropriate separation of potentially-hazardous components. In the end-of-life treatment industry, disassembly has largely been limited to manual labor, which is expensive in developed countries. Automation is one possible solution for economic feasibility. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.

Comprehensive Information Chain for Automated Disassembly of Obsolete Technical Appliances

Advanced Motion Control and Navigation of Robots in Extreme Environments

Advances in robotics and autonomous systems have opened new horizons for the scientists by creating new opportunities to explore extreme environments that would previously not have been possible. For example, robots that are deployed to study environmental processes such remote volcanos, monitor the climate variables under the adverse weather conditions, understand underground mines, and explore deep oceans which are all inaccessible or hazardous for the human. Industrial applications can also often be situated in extreme environments such as offshore oil and gas and nuclear industries. In such applications the autonomous robot is expected to complete tasks such as repair and maintenance, exploration, reconnaissance, inspection, and transportation which is either done in isolation or as a team of cooperative robots. Due to the harsh and severe conditions of such environments, designing an advanced robotic system that can endure them is a challenging task. The robot needs to cope with the time-varying, restricted, uncertain, and unstructured nature of the environment to achieve the planning and execution of the tasks. This in turn demands development of advanced, robust and adaptive motion control and navigation algorithms along with machine learning and deep learning algorithms with high cognitive capability for the robot to perceive the surrounding environment effectively. The use of both single and multi-robot platforms can be advantageous depending on the specific application and environment.