Real-Time Spatiotemporal Tubes for Dynamic Unsafe Sets
Ratnangshu Das, Siddhartha Upadhyay, Pushpak Jagtap
AI summary
Problem
Autonomous systems struggle to safely navigate dynamic, cluttered environments with unknown dynamics while meeting strict time-bound task specifications. Existing real-time methods either lack formal safety guarantees, require full knowledge of future obstacle trajectories, or demand computationally expensive optimization.
Approach
The authors introduce a real-time spatiotemporal tube (STT) framework that defines a time-varying spherical safety tube in the state space. The tube’s center and radius adapt online using only live sensor data, and a closed-form, model-free control law constrains the system to remain inside it.
Key results
- Real-time STT formulation adapting to dynamic obstacles using only current sensor data
- Closed-form, approximation-free control law guaranteeing formal safety and timing constraints
- Successful simulation and hardware validation on mobile robots and quadrotors in cluttered dynamic environments
- Improved computational efficiency and higher success rates compared to state-of-the-art algorithms
Why it matters
Enables safe, real-time navigation for autonomous robots and drones in unpredictable environments without requiring prior knowledge or heavy computation.
Abstract
This paper presents a real-time control framework for nonlinear pure-feedback systems with unknown dynamics to satisfy reach-avoid-stay tasks within a prescribed time in dynamic environments. To achieve this, we introduce a real- time spatiotemporal tube (STT) framework. An STT is defined as a time-varying ball in the state space whose center and radius adapt online using only real-time sensory input. A closed-form, approximation-free control law is then derived to constrain the system output within the STT, ensuring safety and task satis- faction. We provide formal guarantees for obstacle avoidance and on-time task completion. The effectiveness and scalability of the framework are demonstrated through simulations and hardware experiments on a mobile robot and an aerial vehicle, navigating in cluttered dynamic environments.