GLIR: A Practical Global-local Integrated Reactive Planner towards Safe Human-Robot Collaboration

In manufacturing, the current trend-shift from mass-production to mass-personalization is enabled, among others, by the emerging field of human-robot collaboration (HRC), in which humans collaborate or work in proximity with robots. In HRC scenarios, robots need to exert a desired behaviour that maximizes utility without sacrificing safety and responsiveness. To maximize safety and utility in static environments, state-of-the-art offline motion-planners use computationally-heavy algorithms for approximating the collision-free robot reachability and accordingly generate (sub-)optimal robot trajectories. To enable real-time responsiveness, we propose an integrated global planner to generate sub-optimal trajectories. It relies on a closed-loop reactive controller for executing the global plan while ensuring safety with practical assumptions about the environment. We evaluate GLIR in simulation. In our experiments, our global planner operates at 25 Hz and the local planner at 100 Hz, enabling their execution in dynamic environments. In all experiments on static scenes with static and dynamic goals, GLIR keeps a safety distance from obstacles. We showcase some simulation experiments and a real-world demonstration in the video available at https://mohamedgalil.github.io/glir/.