Virtual Force Senor Based on PSO-BP Neural Network for Legged Robots in Planetary Exploration

Abstract

The foot force of the legged robot plays a decisive role in the balance of the fuselage. Especially when walking on the irregular road surface and the less rigid road surface, the change of the foot end support force will change the attitude of the robot body, which will affect the stability. In addition, the change of foot force is also closely related to the flexibility of the movement of the leg of the robot. When the movement of the foot end has a transition from free space to constrained space, only the position control will not meet the requirements of the leg for the flexibility of the movement. This paper addresses the design of virtual sensors for terrain adaptation developed with the aims of simplifying the hardware of the legged robot or increasing the reliability of the sensorial information available. The virtual force sensor (VFS) is developed based on particle swarm optimization (PSO) BP neural network and can estimate the forces exerted by the feet from data extracted from joint-position, joint-velocity, and joint-torque, which are mandatory in all robotic systems. The force estimates are used to detect foot/ground contact. Several simulations carried out with the hexapod robot are reported to prove the efficacy of this method. This method simplifies the hardware of the robot, reduces design, construction and maintenance costs while enhancing the robustness of the robot and the reliability of its behavior.