Mines all over the world are struggling against factors such as safety concerns, labor shortages, and calls to increase productivity. While many are turning to vehicle automation technology as a solution to their challenges, many are finding that the command and control software they are required to use ends up complicating tasks, tying down operators to only one or two vehicles which shackles their break even date and long-term scalability.
The Mobius Haulage A.I. is designed to help offload the tasking of vehicles and help free up operator cognitive resources to handle more vehicles.
Each vehicle in a manned or unmanned haulage system must be able to drive a path to the loading zone, be positioned for loading, drive a path to the dump zone, identify a location to and execute the dump, and effectively interact with any other vehicles also in the cycle (e.g. vehicles may need to develop a queue to allow another vehicle in the load or dump zone to clear before approaching).
Command and control software may be capable of accomplishing all of these tasks, but a single operator would be overloaded with just one or two vehicles in order manually maintain proper vehicle spacing, queuing, and dynamic positioning in the load and dump zones. The Mobius Haulage A.I. will be able to overcome all of these issues by leveraging advanced pathing and tasking algorithms to automatically track and task vehicles in the cycle. The burden of plotting dynamic paths, vehicle interactions, and queuing is handled in a hands-off manner by the software system. In this way, operators take on the role of facilitators, monitoring the effectiveness of the system and engaging with vehicles only as they encounter errors such as an obstacle detection alert.
The Mobius Haulage A.I. is intended significantly decrease the impact of additional vehicles being added to a single operator. As operator capacity increases, so does the overall system scalability and profitability.
The Haulage A.I. is due for release in March of 2015.