Fast LTL-Based Flexible Planning for Dual-Arm Manipulation

In this paper, we propose a method for automatically generating object handling actions based on simple action definitions. The need to replace workers by robots is increasing, and, in fact, many research projects on robots have worked with simple motion definitions. Many applications are for mobile robots such as drones, however, and if such methods are applied directly to object handling, like a pick and place operation, it is necessary for humans to give detailed instructions. Hence, our contribution is to propose a model that simulates the real world with an augmented hybrid system that includes the states of objects. Then, it becomes possible to automatically generate robot motions with simple motion definitions and calculate them within a reasonable time. We demonstrate through computer simulation with a dual-arm robot that robot motions can be generated by simple definitions even if the environment changes to a certain degree.