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In this paper, an optimized central pattern generator (CPG) network is proposed for humanoid walking control. The CPG controller targets three joints (hip, knee and ankle) of each leg including 4 degrees of freedom (DOFs). The connections for CPG units of related joints are simplified and optimized hierarchically. The total number of CPG parameters is greatly decreased in this way. Moreover, the genetic...
This work presents a MNSM (Mirror Neuron System Mechanism) inspired method to control the motion of a dual-arm robot. The new symmetrical action can be classified as instinct-following arbitrary trajectory and goal-based motion; they are planned by the dual-arm robot planner, and implemented by the dual-arm robot within a master/slave protocol. The master arm uses a motion planner to generate corresponding...
The states and actions of the robots in uncertain environments are continuous, which will easily lead to the problem of slow learning speed and the combinatorial explosion issue of the reinforcement learning. Ant colony optimization (ACO) is an evolution algorithm based on swarm mechanism that takes full advantage of the pheromone mechanism to simplify the information sharing and collaborative issues...
For a wirelessly-connected multi-robot system operating in a realistic environment, the wireless communication condition among mobile robots is generally unstable and fluctuating due to the signal loss, attenuation, multi-path fading and shadowing. This paper presents a decentralized control strategy, using the technique of reinforcement learning artificial neural network, to learn and approach a...
The solving strategy of artificial intelligence (AI) is adopted with bottom-up design to solve its hard problems. To tackle end-to-end AI-hard problems, a highly self-adaptive control system-on-chip has been developed to self-learn its internal and external resources with the aid of sets of sensors and actuators. Inspired by biological cell learning theory, different approaches of modelling techniques...
Extreme Learning Machine (ELM) for Single-hidden Layer Feedforward Neural Network (SLFN) has been attracting attentions because of its faster learning speed and better generalization performance than those of the traditional gradient-based learning algorithms. However, it has been proven that generalization performance of ELM classifier depends critically on the number of hidden neurons and the random...
Online path planning for multi-robots in complicated and dynamic environments is a difficult and hot issue in the field of robotics. Many traditional path planning methods cannot meet the requirements of online and real-time processing. Neuro-dynamics-based method has an aptitude for online and real-time path planning in complicated and dynamic environments. However, this method still has shortcomings...
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