RoboPlanner: Towards an Autonomous Robotic Action Planning Framework 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 paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we 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. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses

Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time

Cosys-AirSim: A Real-Time Simulation Framework Expanded for Complex Industrial Applications

Within academia and industry, there has been a need for expansive simulation frameworks that include model-based simulation of sensors, mobile vehicles, and the environment around them. To this end, the modular, real-time, and open-source AirSim framework has been a popular community-built system that fulfills some of those needs. However, the framework required adding systems to serve some complex industrial applications, including designing and testing new sensor modalities, Simultaneous Localization And Mapping (SLAM), autonomous navigation algorithms, and transfer learning with machine learning models. In this work, we discuss the modification and additions to our open-source version of the AirSim simulation framework, including new sensor modalities, vehicle types, and methods to generate realistic environments with changeable objects procedurally. Furthermore, we show the various applications and use cases the framework can serve.Comment: Accepted at Annual Modeling and Simulation Conference, ANNSIM 202

Progress in Autonomous Picking as Demonstrated by the Amazon Robotic Challenge

The automation of the picking process is a longheld dream in intra-logistics. while the expert judgment in recent years was, that there is no immediate applicability to be expected for applications in areas, where the variety of products is high. However, the recent advances in computer science, mechanical engineering and material handling systems design have moved the field substantially forward. In this article we want to show, how the Amazon Robotic Challenge has driven this development process and how capable the technology is. The Amazon robotic Challenge (ARC) and its increasingly difficult challenges are described first. Then the applied technology in soft- and hardware and its performance increase are described. The performance of important participants in the past is estimated and finally the approach of the team IFLpiro is presented. Finally, the impact of the results on the future implementation of material handling systems is discussed