Adaptive Robot Navigation and Control in Dynamic Environments

Robot navigation in dynamic environments presents fundamental challenges as robots must handle unpredictable changes, moving obstacles, and uncertain conditions while maintaining real-time performance and safety. This research addresses the critical problem of developing adaptive navigation systems that can learn and adapt continuously to changing environmental conditions. The core technical innovation lies in integrating learning-based algorithms with traditional navigation approaches, combining reinforcement learning for navigation policy optimization, deep learning for complex pattern recognition, and adaptive model predictive control for trajectory tracking. The methodology employs multi-sensor fusion for robust environment perception, probabilistic representation of dynamic obstacles with uncertainty quantification, and predictive modeling for future state estimation using machine learning techniques. This framework enables real-time parameter adaptation based on performance feedback, dynamic algorithm switching based on environmental conditions, and safe exploration strategies specifically designed for physical robot deployment in complex, changing environments.

The system delivers significant benefits across service robotics and industrial applications, where robots must navigate safely around humans and adapt to changing layouts. Hospital robots implementing this technology successfully navigate around medical staff and patients while maintaining safety protocols, office robots provide reliable assistance in busy workplace environments, and warehouse robots adapt efficiently to changing inventory layouts and traffic patterns. Industrial applications include factory robots that safely navigate around human workers, delivery robots operating in dynamic urban environments, and inspection robots that adapt to complex facility layouts. The technology enables superior collision avoidance, improved path efficiency, and robust operation under environmental uncertainty while providing rapid adaptation to new environments and tasks. Carnegie Mellon’s research team seeks partnerships with robotics companies and system integrators interested in deploying next-generation adaptive navigation systems that combine learning capabilities with real-world robustness for practical autonomous robot applications.

For complete technical details and experimental results, please refer to the original publication: 18-iros-deng.pdf