This paper describes a new locomotion mode to use in a crawling robot, inspired of real inchworm. The crawling device is modelled as a mobile manipulator, and for each step of its motion, the associated dynamics relations are derived using Euler-Lagrange equations. Next, the Genetic Algorithm (GA) is utilized to optimize the trajectory of the free joints (active actuators) in order to minimize the consumed effort (e.g. integral of square of torques over the step time). In this way, the results show a reduction of 5 to 37 percent in torque consumption in comparison with the gradient based method. Finally, numerical simulation for each step motion is presented to validate the proposed algorithm.
Abstract-In recent years, researches on adaptive control have focused on bio-inspired learning techniques to deal with real-life applications. Reinforcement Learning (RL) is one of these major techniques, which has been widely used in robot control approaches. The implementation of artificial neural networks in RL algorithms enables more efficient optimal control strategies. This article proposes a field application of neural network reinforcement learning (NNRL) for walking control of an active simulated 3-link biped robot. The adaptive control agent consists of two neural network units, known as actor and critic for learning prediction and learning control tasks. Results of the presented control method reveal its efficiency in stable walking control of the biped robot model as a nonlinear complex dynamic task.Index Terms-Adaptive control, biped robot, neural network reinforcement learning,stable walking.
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