Robust Perception and Navigation for Autonomous Mobile Robots

This research develops robust perception and navigation algorithms for autonomous mobile robots operating in challenging and dynamic environments, addressing critical challenges in environmental variability, sensor limitations, dynamic obstacles, and safety requirements in human-shared spaces. The work creates advanced multi-modal sensor fusion systems featuring visual-inertial odometry (VIO), LiDAR-camera fusion, GPS integration, and sensor failure detection with graceful degradation capabilities. Key technical innovations include Kalman filtering with outlier rejection, particle filtering for non-linear estimation, probabilistic roadmaps with uncertainty consideration, hierarchical planning architectures with global and local motion planning, adaptive algorithms for varying lighting and weather conditions, moving obstacle detection with trajectory prediction, human-robot interaction with social navigation, and comprehensive safety frameworks with multi-level monitoring and emergency intervention systems.

Industrial automation and service robotics applications benefit from autonomous material transport, inventory management, hospital navigation, shopping mall assistance, and public space maintenance with enhanced reliability and safety performance through demonstrated improvements in success rates under various environmental conditions and reduced safety incident rates. The framework enables transformative applications across manufacturing warehouses, healthcare facilities, retail environments, airports, and educational institutions with successful validation in crowded urban environments, industrial settings, and mixed indoor-outdoor scenarios through multi-month deployment studies and seasonal variation testing. Strong industry partnerships facilitate technology transfer and validation through real robotic systems, with applications spanning from predictive failure detection and self-diagnosis capabilities to cybersecurity resilience and formal safety validation. The team’s expertise in multi-modal sensor fusion, adaptive navigation strategies, uncertainty-aware planning, and safety-certified robotics positions them to advance next-generation autonomous systems and seek collaboration opportunities for deep learning perception, reinforcement learning navigation policies, and human-robot trust frameworks.

Acknowledgments

This work was supported by autonomous systems research grants and collaborations with robotics industry partners. We thank our research collaborators for providing access to challenging testing environments and long-term validation opportunities.

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