CURRENT RESEARCH PROJECTS

A developmental network, called Where-What Network (WWN), is designed for a general sensorimotor pathway, such that recognition and attention interact with each other in a single network. The cortex inspired neuromorphic architecture models three types of attention: feature-based bottom-up attention, location-based top-down attention, and object-based top-down attention. The in-place learning algorithm is used to develop the internal representation (including synaptic bottom-up and top-down weights of every neuron) in the network, such that every neuron is responsible for the learning of its own signal processing characteristics within its connected network environment, through interactions with other neurons in the same layer.

Object Classification by the Sensor Fusion of Radar and Vision Systems

A low cost, real-time object recognition system is developed in a sensor fusion framework. The powerful vision algorithm is used to deal with a wide variation of targets provided by radar system. The objects of interest fall into three categories:(1) vehicle (e.g., cars and trucks); (2) pedestrians; (3) other objects having radar returns (e.g., light poles, guide rails, signs and bridges, garbage cans, road barriers). The module clusters multiple radar tracks into a single object and acilitates the threat assessment algorithms by offering object category labels. It fits into an embedded system and holds promises in industrial applications. 

Sensor-based Navigation for Autonomously Driving Urban Vehicle

Several sensors were applied to percept local environments during the vehicle driving. The environmental perception is achieved by a combination of vision, LADAR, ultrasonic, and contact sensors. For the autonomous driving, primary navigation is provided by a DGPS, commercial correction services and an Inertial Measurement Unit. The work will be partially used in the DARPA Urban Challenge, 2007. 

Autonomous Mental Developmental Learning

This research is to advance artificial intelligence using what we call the developmental approach. The new approach is motivated by human cognitive and behavioral development from infancy to adulthood. It requires a fundamentally different way of addressing the issue of machine intelligence. Developmental programs, such as SHM, IHDR, MILN, etc., was applied for a robot to develop its mind through the learning process. 

PREVIOUS RESEARCH PROJECTS

Autonomous Outdoor Navigation

Developed an online-learning and attention-based approach to outdoor navigation in DARPA Grand Challenge, 2005. The algorithms were used on Team AVS and Team Crossland in  the challenge events.

Visual System of Rock Core Image

Developed a visual system for rock core image analysis, in order to obtain physical parameters of geologic samples.