Constrained Environment

Constrained environment research focuses on developing methods for effective navigation, manipulation, and control within spatially limited or complex settings. Current efforts utilize diverse approaches, including graph-based path planning, deep neural networks for shape estimation and control (often incorporating uncertainty quantification), and reinforcement learning for optimizing agent behavior in dynamic, obstacle-filled environments. This research is crucial for advancing robotics, particularly in minimally invasive surgery, industrial automation, and multi-agent systems, by enabling safer, more efficient, and adaptable operations in challenging real-world scenarios.