Autonomous Navigation System of Hexapod Robot based on Fuzzy Neural Network

Abstract

A closed-loop autonomous navigation system based on fuzzy neural network is proposed to deal with the autonomous navigation issue of hexapod robot in unknown environment. The closed-loop system is designed in order to optimize the output performance. The navigation algorithm is designed to combine fuzzy control with neural network. Fuzzy control is used to realize the ability of logical reasoning and neural network is conducive to learning and training ability. The ambient sensors are a GPS sensor, an electronic compass sensor and an ultrasonic sensor with sector scanning state. These sensors can complete the detection of the surrounding obstacles, the target course angle and the current course angle. The performance of the robot's autonomous navigation system is compared with the open-loop and closed-loop system based on fuzzy neural network in the simulation experiment, which demonstrates that walking time based on closed-loop system significantly declines compared to the open-loop system, meanwhile, the traveling speed also improves. In the way to the destination, the robot can safely and quickly bypass the obstacles without any redundant paths. Kinematics Model AnalysisEnvironmental information acquisition and optimization. The mechanical structure of the hexapod robot is shown in Fig.1. The robot's perception of the surrounding environment is realized by sensors, therefore the types and distribution of sensors are very important for the whole system. This paper selects ultrasonic sensor, GPS sensor and electronic compass sensor as the perceptrons. In order to solve the problem that ultrasonic sensor can not accurately obtain the boundary

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

“…In this paper, the tactile force-feedback system is developed and installed on the foot ends of the hexapod robot. The structure of the hexapod robot (Li et al, 2016) is shown in Figure 2(a). Its leg structure includes the arm steering gear, leg steering gear, foot steering gear, and foot end.…”