Integrated process planning and scheduling (IPPS) is a good way to achieve a global improvement for the performance of a manufacturing system, it has been extensively researched over the past years and it continues to attract the interest of both academic researchers and practitioners. This paper first summarizes the critical problems of IPPS and then a survey of the integrated model and optimal implementation method for IPPS is presented. The integrated model is categorized into interface-oriented integration and function-oriented integration two types based on the integration object and are discussed in detail respectively. The deficiencies of the current integrated models and the suggestion for further improvement are also given. The integration of process planning and scheduling has been implemented by variety methods and agent-based approach is discussed in more detail. Finally, future research directions and conclusions in IPPS research are discussed.

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.

No abstract