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The purpose of this study is to test a chassis robot on rugged road cargo handling.

Attitude solution of D-H series robot gyroscope speed and acceleration sensor.

In identical experimental environments, hexapodal robots experience smaller deviations when using a four-footed propulsive gait from a typical three-footed gait for forward motion; for the same distance but at different speeds, the deviation basically keeps itself within the same range when the robot advances forward with four-foot propulsive gait; because the foot slide in the three-footed gait sometimes experiences frictions, the robot exhibits a large gap in directional deviations in different courses during motion; for motion using a four-footed propulsive gait, there are minor directional deviations of hexapodal robots resulting from experimental errors, which can be reduced through optimizing mechanical structures.

Planning different gaits can solve problems existing in some typical gaits. This article has put forward a gait planning method for hexapodal robots moving forward with diverse gaits as a redundant multifreedom structure. Subsequent research can combine a multiparallel-legged structure to analyze kinematics, optimize the robot’s mechanical structure and carry out in-depth research of hexapod robot gaits.

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1.

The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.