Hexapod Free Gait Generation Based On Discretization

As a research hotspot of robotics,hexapod robot technology has always been concerned by many researchers.Hexapod robots have the ability to adapt the terrain,walk steadily and have redundancy in the legs,which make them suitable for tasks which have strict demands for independency and reliability such as spying in the wild,searching underwater and exploring the outer space.However,there are some problems in gait planning.For example,free gait algorithm of the robot can be adjusted according to the sequence of the legs and time to adapt to the environment,but the algorithm is relatively complex and more calculation;hexapod robot can walk quickly stable on flat surface with the triangle gait which can be controlled simply With this problems,this paper mainly studies an algorithm to make robot efficiently freedom-walk in the complex environment where there are both flat surface and obstacles.The main innovation work is as follows:Firstly,According to the physiological structure of the Japanese carpenter ant,a six-legged robot is designed.The kinematics model is established and the relationship between leg position and the joint angle is carried out.To verify the rationality of structure design,the robot posture adjustment and triangle gait simulation are simulated in ADAMS.Secondly,when the hexapod robot walks,the work space of the foot is analyzed,which is a segment.The foot trajectory is concluded based on the interpolation.The simulation shows that the foot trajectory is smooth when the robot works.Thirdly,this paper presents a hexapod free gait generation based on the discretization.On the flat ground,the hexapod robot can walk at high speed with tripod gait.When there are obstacles,the second gait are used to cross the obstacles.The second kind of gait is based on discretization model,according to the size of obstacles,the stroke is adjusted.Finally,the hexapod robot experiment platform is put up,one leg of the robot movement ability is experimented.The robot is tested in the flat and rough surface and it proves the gait planning method works out.