EvoBot Munich was developed to operate in cargo terminals and on the apron at airports. It is an autonomous mobile robot with gripper arms and a sense of balance intended to help bridge the manpower gap and improve efficiency in cargo operations.
Exceptional performance can be demonstrated by Large Language Models (LLMs) in a variety of tasks, including essay writing and question answering. Researchers at Google created PaLM-E to use an “embodied” LLM, trained with sensor data as well as text, to control a robot. LLMs can generate text that lets them plan and reason out their ideas.
The several industries impacted by the evoBot are:
- Air Freight Industry: Autonomous vehicles and robots are expected to impact the industry in times of a shortage of skilled workers. In cargo terminals, evoBOT is designed and intended to help bridge the manpower gap and improve efficiency in cargo operations. Cargo operations can be automated with evoBOT to help reduce the time and cost associated with cargo handling, leading to lower costs for consumers.
- Robotics Industry: With evoBOT, new applications, and use cases will be discovered and designed to lead the development of new technologies and products that benefit the robotics industry and other industries as well.
- Logistics Industry: It can reduce the risk of injury to workers and damage to cargo, lowering costs for companies and allowing safe handling of the cargo. The logistics industry is also a key contributor to social and environmental sustainability. The industry is also becoming customer-centric and evoBOT can improve the speed and accuracy of cargo handling operations to lead to a better customer experience.
Efficiency improvement, safety, and providing a new platform for innovation support the evoBOT to drive progress and growth in the automotive industry and beyond.