Researchers Granthik Halder, Rudrashis Majumder, Rakshith M R, Rahi Shah, and Suresh Sundaram from the Indian Institute of Science have developed a novel framework called NeuroHJR that aims to improve the real-time obstacle avoidance capabilities of autonomous ground vehicles (AGVs). Their work, published in the journal IEEE Transactions on Robotics, addresses the challenges of navigating safely in complex environments while accounting for dynamic and uncertain conditions.
Autonomous ground vehicles, such as those used in mining, agriculture, and warehouse logistics, must navigate safely through cluttered environments. Traditional Hamilton-Jacobi Reachability (HJR) methods provide safety guarantees by calculating forward and backward reachable sets, but they struggle with scalability in environments with many obstacles due to the need for grid-based discretization. This limitation can hinder real-time decision-making and operational efficiency.
The researchers introduced NeuroHJR, a framework that utilizes Physics-Informed Neural Networks (PINNs) to approximate HJR solutions. By integrating system dynamics and safety constraints directly into the neural network’s loss function, NeuroHJR eliminates the need for grid-based discretization. This approach allows for efficient estimation of reachable sets in continuous state spaces, significantly reducing computational costs while maintaining safety performance comparable to classical HJR solvers.
The effectiveness of NeuroHJR was demonstrated through simulations in densely cluttered scenarios. The results showed that the framework achieves robust obstacle avoidance and safety performance, making it a promising solution for real-time applications in the energy sector and beyond. For instance, in the energy industry, autonomous vehicles are increasingly used for tasks such as inspection, maintenance, and material transport in environments like oil and gas fields, solar farms, and wind farms. The ability to navigate safely and efficiently in complex environments can enhance operational safety, reduce downtime, and improve overall productivity.
In summary, NeuroHJR offers a scalable and efficient solution for real-time obstacle avoidance in autonomous ground vehicles. By leveraging the power of neural networks and integrating safety constraints, this framework paves the way for safer and more reliable autonomous operations in various industries, including the energy sector. The research was published in the IEEE Transactions on Robotics.
This article is based on research available at arXiv.