Abstract: Contact force distribution algorithm (CFDA), which aims at optimizing contact forces for each supporting legs, could increase the traverse ability, enhance walking stability and reduce energy consumption for hexapod robot under uneven terrain by setting up constraint model and designing object function. A new decomposed quadratic programming (QP) algorithm to enhance the slope-climbing ability has been proposed, by designing an object function aiming to decrease the maximum tractive coefficient of supporting legs and setting up constraint model for hexapod robot. The new decomposed QP algorithm first distributes normal contact forces and then distributes tangential contact forces. The two steps are all solved by convex optimization, so the new algorithm could be used in real time control. Simulation platform is built and relative simulations have been done about hexapod walking on different gaits using this new decomposed QP algorithm. The results show that the maximum climbing angle of decomposed QP algorithm is more higher compared to traditional pseudo-inverse method. Robot prototype named ElSpider is used to verify the performance of the new method. Experiment results show that the new method could decrease the maximum of tractive coefficient under same climbing angle.

Key words: hexapod robot, contact force distribution, decomposed quadratic programming, slope-climbing ability