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Risk-informed navigation for autonomous patient transport can cut collision rates by over 40%, revolutionizing safety in medical emergencies.
A robot can autonomously navigate emergency evacuations, achieving a 92.4% success rate in facilitating door traversal and equipment delivery.
Achieve faster and more reliable robot motion planning by reactively switching between simplified models within a Nonlinear Model Predictive Control framework, guided by deep reinforcement learning.
