AIDrivers emphasises digital twin in PSA Singapore project

A busy port in Sunset glow

AIDrivers has been working with PSA Singapore to develop Autonomous Prime Movers (APM) at PSA’s Pasir Panjang Terminal.

Since being awarded an APM proof of concept (PoC) project in October 2018, AIDrivers has been working on how to bring the new technology into an operational terminal.

While the tender ultimately focused on the APMs, it has expanded beyond that to involve digital twin technology as an enabling solution to the development of autonomous prime movers integration at a brownfield terminal.

“This project focuses on retrofitting existing terminal diesel trucks for autonomous mobility operations at a brownfield site. Traditional automation such as Automated Guided Vehicle (AGV) works well in greenfield sites but requires substantial modification to the environment and infrastructure in the brownfield terminal,” explained AIDrivers Founder, Dr Rafiq Swash, in an exclusive interview with PTI.

“In our case, our autonomous mobility solutions can be deployed into any existing environment little or no change.”  

PSA describes the terminals at Pasir Panjang as PSA’s “most advanced” and already feature new technologies such as fully-automated, zero-emission electrical yard cranes.

Since AIDrivers was awarded the PoC tender, two diesel trucks have been converted to fully autonomous trucks using AIDrivers’ Ai enabled autonomous drive platform and have been running on trials since 2019.

Dr Swash explained that this is a strategic project, rather than just a developmental one. Since beginning the project in 2018 Dr Swash said it is progressing as expected though could not detail any particular milestones.

“A project of this nature is beyond the milestones and we are very much committed to continuing collaborate with PSA on this journey,” he said. “The future is bright.”

The company has also been continuing the project with steady progress even during the COVID-19 pandemic. With headquarters in the UK and regional offices in Singapore, Dr Swash said that the impact of the COVID-19 pandemic has allowed AIDrivers to enable the Singapore office to progress on the project execution by scaling up the Singapore team significantly during 2020.

Digital Twin technology

The use of data and converting information into knowledge to optimise operations in real time and long term is something that has been identified as a gap by AIDrivers, and many other suppliers, in the ports industry. Especially when compared to other industries like finance and banking.

“[Ports and terminals] have many huge cranes and horizontal transporters and their focuses are to move containers from A to B. Most of the terminal operators have done limited work to use data collected to improve their operations,” Dr Swash explained.

The use of AIDriver’s digital twin is applicable when there is a need to integrate the technology without disrupting the operations in the operational brown field terminal such as PSA Pasir Panjang Terminal.

“The digital twin creates awareness of autonomously operated vehicles, showcases different sophisticated used cases which is difficult or not possible to reconstruct in the real-world environment due to the risk. In addition, we have the same autonomous algorithms in our digital twin that drives the real truck on the ground at the terminal,” he explained.

“For our solutions, it doesn’t matter if it drives the real truck or the virtual truck in the truly reflected environment and scale.”

Dr Swash said the use of digital twin will become a real novel technology for ports who want to reconfigure their operations without the initial need to invest on new trucks or equipment.

“We have designed our digital twin to accelerate autonomous mobility development but now we realise that it can enable the port operators to be able to go beyond what they are doing today.”

The AIDriver’s digital twin can be utilised to simulate future scenarios to enable ports to re-evaluate their data, efficiency and resource requirements. Ports can recreate virtual scenarios with different environmental configurations, different fleets and different levels of autonomy.

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