Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantum computation and quantum information
Quantum computation and quantum information
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
An intelligent mobile vehicle navigator based on fuzzy logic andreinforcement learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.