AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Agv parking robot1/21/2024 Springer.Ĭhen, Y., Zhuang, L., Zhu, L., Shao, X., & Wang, H. In 2006 Pacific-Asia conference on knowledge discovery and data mining, pp. In: 2021 IEEE 4th international conference on computer and communication engineering technology (CCET), pp. Cooperative task allocation of multiple via vs based on greedy algorithm. Journal of Manufacturing Systems, 53, 32–48.Ĭhen, Y., Du, C., Chen, J., & Yu, W. Multi-objective particle swarm optimization for multi-workshop facility layout problem. Scrum task allocation based on particle swarm optimization, pp. In: 2nd IBM symposium on mathematical foundations of computer science, pp. Complexity of the deadlock avoidance problem. Simulation results show that the proposed method significantly improves efficiency, up to 20–30% as compared with traditional methods.Īraki, T., Sugiyama, Y., & Kasami, T. Cases in which wrong predictions have been made are also addressed in the proposed method. The proposed approach aims at reducing the preparation time by predicting the starting locations for future tasks and then making decisions on whether to send an AGV to the predicted starting location of the upcoming task, thus reducing the time spent waiting for an AGV to arrive at the starting location after the upcoming task is created. Typically, AGVs have to travel to designated starting locations from their parking locations to execute tasks, the time required for which is referred to as preparation time. In this paper, we propose an approach to improve the efficiency of traditional deadlock-free scheduling algorithms. Finding ways to improve efficiency while preventing deadlocks is a core issue in designing AGV systems. The drive remains energized to streamline restart.Automated guided vehicles (AGVs) are driverless robotic vehicles that pick up and deliver materials. The Gold Drum drive then invokes STO, immediately preventing the motor from generating torque and an external brake stops the vehicle. If one of the sensors detects an obstacle, it sends an alert to the drive over the EtherCAT network, which operates with a cycle time of 100 µs or better. Ray scans the surroundings 100 times per second to maintain situational awareness and check for obstacles and personnel. The Gold Drum drives communicate with the sensors and feedback devices over an EtherCAT network.Įlmo’s Gold Drum drives are equipped with safety functionality in the form of safe torque off (STO). To navigate to a planned destination, Ray uses optical sensors and retro reflectors to communicate wirelessly with the central controller an average of 6000 times in a single trip. Elmo’s Gold Drum drive can handle a variation in source voltage ranging from 14-95 VDC, enabling the drives to be effective throughout the charge lifetime. Power quality varies over the discharge cycle of a battery, so the minimum supply voltage of a drive is just as important as the maximum. Elmo drives operate with >99% efficiency, enabling the Ray to operate for up to eight hours between charges. The drive delivers 15 kW of continuous output power from a package weighing just 700 g. The Serva team chose Elmo’s 150 A/100 V Gold Drum to meet its motion control requirements. The Elmo Motion control solution included:
0 Comments
Read More
Leave a Reply. |