ByteDance Proposes Astra Dual-Model Architecture for Robot Navigation
ByteDance unveils Astra dual-model architecture enabling autonomous robot navigation in complex indoor environments with 99.9% accuracy.
"RoboPub" Publication: 20% Discount Offer Link.
In today's era of rapid technological development, robots are being applied increasingly widely across various fields, from industrial production to daily life, where their presence can be seen everywhere.
However, modern robot navigation systems face numerous challenges in diverse and complex indoor environments, with the limitations of traditional methods becoming increasingly apparent.
Traditional Navigation Bottlenecks Emerge, Astra Rises to the Occasion
In complex real-world environments, mobile robots must solve three major challenges to move safely and reliably: Where do I want to go? Where am I? How do I get there? These are precisely the three core navigation problems: goal localization, self-localization, and path planning.
For goal localization, in some applications, targets may be specified through natural language or target image prompts, requiring the system to understand the prompts and locate targets on the map.
Self-localization requires robots to determine their own position on the map, especially in complex scenarios like warehouses that are highly repetitive and lack global landmarks, where traditional navigation systems often rely on artificial landmarks such as QR codes.
Path planning is divided into global planning and local planning, where global planning generates rough routes based on robot pose and target pose, while local planning is responsible for reaching intermediate waypoints on the global path while avoiding obstacles.
To solve these tasks, traditional navigation systems typically consist of multiple modules, containing multiple small models or rule-based systems.