Advanced Robotics and Autonomous Systems for Complex Environment Navigation: PhD Thesis Proposal

This PhD thesis proposal addresses the fundamental challenge of enabling autonomous robots to navigate safely and efficiently in complex environments with dynamic obstacles, uncertain conditions, and cluttered geometries. The core technical approach develops novel algorithms across four interconnected components: robust perception through multi-modal sensor fusion and deep learning for object detection and semantic understanding; intelligent planning using dynamic path planning, multi-objective optimization, and risk-aware planning with safety constraints; adaptive control systems featuring model predictive control, robust control under uncertainty, and human-aware control for shared environments; and learning frameworks incorporating reinforcement learning, imitation learning, transfer learning, and online adaptation. The innovation lies in integrating these components into a comprehensive autonomous navigation system that combines theoretical rigor with practical validation through simulation studies, physical robot experiments, and real-world deployment in diverse environments including human-shared spaces.

The practical applications span autonomous vehicles, service robots, and assistive technologies, where safe navigation in complex environments is critical for user acceptance and commercial viability. This research offers significant benefits through enhanced mobility for elderly and disabled individuals, improved safety in transportation and logistics, and increased efficiency in healthcare and service delivery. The proposed work supports the advancement of autonomous systems technology while addressing critical safety and reliability concerns through formal verification methods and comprehensive validation protocols. The research team’s expertise in robotics, artificial intelligence, and control theory, combined with access to Carnegie Mellon University’s Robotics Institute facilities and industry partnerships, positions this work to make substantial contributions to both academic knowledge and practical applications in autonomous navigation, with potential for technology transfer through startup formation, software commercialization, and industry collaboration across robotics companies and application domain partners.

Acknowledgments

This PhD thesis proposal is developed with guidance from advisor Professor Kenji Shimada and support from the Carnegie Mellon University Robotics Institute. We acknowledge industry partners for their collaboration and validation opportunities.

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For complete technical details, please refer to the original publication.