In a recent study published in Satellite Navigation, a group of researchers from Shanghai Jiao Tong University presented a ground-breaking solution by introducing the BIG (Brain-Inspired Geometry-awareness) framework. This has been presented to transform autonomous exploration and navigation in challenging environments.
The expanding process diagram when a brain-inspired agent begins the autonomous exploring process and builds the experience map. This framework draws inspiration from the experience mapping structure that is built during the locomotion of both mammals and human beings. During a typical autonomous exploration shown in the figure, a brain-inspired agent employs a suite of sensors, including camera and Lidar, for the perceptual of its surroundings. Image Credit: Satellite Navigation
The BIG framework greatly lowers computational requirements while increasing efficiency in long-range exploration and navigation tasks by simulating the spatial navigation mechanisms present in mammals.
BIG represents a significant breakthrough in autonomous navigation, potentially revolutionizing robotics, autonomous vehicles, and other domains where effective, resource-conscious navigation is crucial. It can cover unknown areas more quickly with fewer nodes and shorter paths.
In robotics and artificial intelligence, autonomous navigation has long been a major challenge, especially in challenging, unfamiliar environments. Traditional navigation systems frequently fall short when it comes to striking a balance between resource consumption and efficiency.
Although they have demonstrated promise, brain-inspired navigation models—which mimic mammal spatial awareness usually have limited scalability and frequently perform poorly on long-range exploration tasks.
This disparity has made it necessary to investigate further how biological concepts can be combined with cutting-edge navigational technologies to overcome these obstacles.
The BIG framework significantly increases efficiency and resource usage by fusing autonomous exploration tasks with brain-inspired geometry cell models, establishing a new benchmark for autonomous navigation in difficult environments.
The BIG framework, which combines state-of-the-art exploration and mapping techniques with brain-inspired spatial perception, represents a substantial advancement in autonomous navigation.
Fundamentally, BIG mimics mammalian navigation processes using the geometry cell model, allowing for a more flexible and effective method of navigating challenging environments. The four main parts of the framework are BIG-Explorer, BIG-Navigator, BIG-Map, and Geometric Information.
BIG-Explorer maximizes exploration and expands frontiers with the least computational work by allocating geometric parameters prioritizing boundary information. Then, using information acquired during exploration, BIG-Navigator directs autonomous agents to the desired locations, guaranteeing accurate navigation.
In the meantime, BIG-Map maximizes efficiency by decreasing storage space and enhancing scalability while producing experience maps via spatiotemporal clustering.
One of the BIG framework's most notable innovations is its ability to reduce computational requirements by at least 20% while preserving reliable coverage and effective navigation compared to current approaches.
The framework is especially well-suited for long-range tasks with limited computational resources. It ensures faster exploration with fewer nodes and shorter paths through real-time boundary perception and optimized sampling techniques.
By incorporating brain-inspired navigation mechanisms, we can achieve far more efficient and scalable solutions for long-range exploration. This approach not only boosts performance but also reflects the natural efficiency inherent in biological systems, pushing the boundaries of what autonomous navigation systems can achieve.
Dr. Ling Pei, Study Lead Researcher, Shanghai Jiao Tong University
The BIG framework has wide-ranging effects on autonomous vehicles, robotics, and space exploration domains. It is a great option for applications with limited processing power and energy because it can effectively navigate complex environments while preserving computational resources.
This is a significant step toward more intelligent, effective autonomous systems. Future research will concentrate on scaling the framework for even larger environments and integrating learning-based techniques to improve its performance further.
Journal Reference:
Sun, Z., et al. (2025) BIG: a framework integrating brain-inspired geometry cell for long-range exploration and navigation. Satellite Navigation. doi.org/10.1186/s43020-024-00156-3.