Postdoctoral Projects at the University of Guelph (in process):
Human-robot Collaborations via Integrated Digital Twins of Robotic and Human Operators
A novel framework is proposed for human-robot collaborations based on integrated digital twins of robotic and human operators. Robotic systems can be enhanced by employing human digital twins based on multimodal data from humans that can provide more intelligent actions, responses, and strategies to collaboratively work with humans in complex and uncertain environments.
Ph.D. Projects at the University of Guelph:
Fish-inspired Self-adaptive Approaches of Swarm Robots
A novel fish-inspired self-adaptive approach is proposed for collective motion for the multi-robot system. Simulation and experimental results demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in dynamic environments.
Feature Learning-based Real-time Rescue of Multi-Robot Systems
A novel feature learning-based bio-inspired neural network is proposed to quickly generate a heuristic rescue path in complex and dynamic environments. Several simulations and experiments have been conducted to evaluate the proposed approach and results show that the proposed approach would significantly improve the speed, efficiency, and optimality for rescue operations.
Real-time Evasion of Multi-Robot Systems in Complex Environments
A novel neurodynamics-based approach is proposed that approximates a general pursuit-evasion game instead of formulating the problem as a differential game. Simulation results indicate that the proposed approach is effective and efficient in complex and dynamic environments.
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