UAVs

An OptimumT case study in curiosity-driven artificial intelligence Coordinating autonomous aircraft requires continuous decisions in a changing environment. A moving target changes direction. Tracking aircraft must adjust their flight controls. The learning system must turn those interactions into increasingly effective behaviour. This project brought those capabilities together in a configurable simulation testbed. Combining curiosity-driven reinforcement […]

Innovation Case Study How OptimumT is Building AI Systems That Learn, Adapt, and Collaborate Executive Summary Autonomous drones have evolved rapidly over the past decade. They can navigate predefined routes, avoid obstacles, and execute increasingly sophisticated missions. Yet one fundamental challenge remains largely unsolved: How do autonomous aerial systems learn to cooperate in environments that […]

What if machines didn’t just follow instructions—but wanted to explore? What if autonomous systems could push beyond rigid programming and begin to learn with intent, adapt with purpose, and act with curiosity? At OptimumT, this is not a distant idea. It’s a direction we are actively building toward. 🚁 The Challenge: Intelligence in the Real […]

Hello, OptimumT followers and innovators! We are thrilled to share some insights into a project that perfectly aligns with our mission at OptimumT to deliver cutting-edge AI solutions. We’re talking about truly groundbreaking research that is poised to revolutionize the world of autonomous aerial systems. This work introduces a paradigm shift in how we approach […]

We have been publishing about grammatical evolution in quite a lot of detail in the past. Here is another one. In this work, we have developed the ability to evolve multi-input multi-output neural networks using grammatical evolution. The main target application that we foresaw was the domain of unmanned aerial vehicles and driverless cars. Both […]

Recently we released Blizzard. It is capable of doing hybrid optimization using GELAB. Hybrid optimization is an important family of learning algorithms. Its main benefit in plain words is that it is helpful for training machines to perform intricate tasks. Its applications are well documented. Recently we had the pleasure of getting our work on […]

Recently we have been involved in the development of multiple-input multiple-output (MIMO) neural networks. These can be useful for training intelligent controllers. And that was our focus too. We aim to use them for the design of smart controllers for driverless cars as well as unmanned aerial vehicles. By implementing this capability we have taken […]

Hi folks, We have made the source code of NUAV testbed available in the public domain. Now you can clone the repository and play with it. Detailed instructions are given in the readme files. Happy droning! Adil Raja / NUAV GitLab.com   Photo by Ondorn

We have been working on simulating autonomous drones for quite some time now. We published details of our work in the past. Recently, we thought about publishing our experiences with the feat of simulating drones. Autonomous unmanned aerial vehicles (UAVs) have been a hot topic in academic circles lately. Enormous literature can be found on […]