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This paper introduces a real-time control framework for navigating unknown Euler-Lagrange systems in dynamic environments, addressing the challenges posed by limited actuator inputs. By extending the spatiotemporal tube (STT) framework to incorporate input constraints, the authors derive offline-verifiable feasibility conditions that ensure safe navigation while adhering to actuator limits. The approach is validated through simulations and hardware experiments, showcasing its effectiveness in achieving finite-time reach-avoid-stay (FT-RAS) specifications without requiring accurate models or online optimization.
Achieving safe navigation in dynamic environments is possible even with unknown system dynamics and limited actuator inputs, thanks to a novel control framework that respects constraints.
Safe navigation in dynamic environments is challenging when system dynamics are unknown and actuator inputs are limited. Existing methods either rely on accurate models, require online optimization, or do not explicitly account for input constraints. This paper presents a real-time control framework for unknown Euler-Lagrange systems that guarantees finite-time reach-avoid-stay (FT-RAS) specifications while respecting actuator limits. We extend the spatiotemporal tube (STT) framework by incorporating input constraints into the controller design and derive offline-verifiable feasibility conditions that relate the available control authority to the tube design and uncertainty bounds. The resulting framework is approximation-free and computationally efficient, making it suitable for real-time implementation. The proposed approach is validated through simulations on a mobile robot, a quadrotor, and a spacecraft, together with hardware experiments on a mobile robot, demonstrating safe navigation while satisfying actuator constraints.
