Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain, and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases that may be queried during planning and execution. In this chapter, the authors propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, they model the knowledge of world models, robotic capabilities, and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. They demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.