Dr. Junlin Ou from MTSU’s Department of Engineering Technology presents a series of studies on real-time edge computing for autonomous systems, focusing on algorithmic development and hardware implementation for intelligent robotic applications.
The research encompasses indoor positioning, path planning, and real-time decision-making in dynamic environments. A recent highlight is the development of a GPU-enabled evolutionary dynamic programming (EDP) algorithm that formulates path planning as a Markov decision process and integrates parallel optimization on edge devices such as the Jetson AGX Xavier. This approach enables rapid re-planning and robust navigation in environments with moving obstacles, achieving path updates at a rate of approximately 0.1 seconds/path. Together, these efforts demonstrate how combining algorithmic innovation with edge computing hardware can significantly enhance the autonomy, adaptability, and computational efficiency of robotic systems operating in real-world, dynamic conditions. See the video here.