Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods.In this paper,a novel biomimetic soft robot driven by Shape Memory Alloy(SMA)with light weight and multi-motion abilities is introduced.We adapt deep learning to perceive irregular targets in an unstructured environment.Aiming at the target searching task,an intelligent visual servo control algorithm based on Q-leaming is proposed to generate distance-directed end effector locomotion.In particular,a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing errors.In addition,the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are presented.Our framework enables the trained soft robot to take actions and perform target searching.Realistic experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.

Determining node duty cycle using Q-learning and linear regression for WSN

Wireless sensor network(WSN)is effective for monitoring the target environment,which consists of a large number of sensor nodes of limited energy.An efficient medium access control(MAC)protocol is thus imperative to maximize the energy efficiency and performance of WSN.The most existing MAC protocols are based on the scheduling of sleep and active period of the nodes,and do not consider the relationship between the load condition and performance.In this paper a novel scheme is proposed to properly determine the duty cycle of the WSN nodes according to the load,which employs the Q-leaming technique and function approximation with linear regression.This allows low-latency energy-efficient scheduling for a wide range of traffic conditions,and effectively overcomes the limitation of Q-learning with the problem of continuous state-action space.NS3 simulation reveals that the proposed scheme significantly improves the throughput,latency,and energy efficiency compared to the existing fully active scheme and S-MAC.