Hexapod Robots: New CPG Model for Generation of Trajectories

Legged robots are often used in a large variety of tasks, in different environments. The large number of degrees-of-freedom, to be controlled during these tasks, turns the online generation of trajectories in these robots very complex. In this paper, we consider a modular approach to online generation of trajectories, based on biological concepts, namely Central Pattern Generators (CPGs). We introduce a new CPG model for hexapod robots’ rhythms, that generalizes the work of Golubitsky, Stewart, Buono and Collins (1998,1999). Each neuron/oscillator in the CPG consists of two modules/primitives: rhythmic and discrete, that are modeled by nonlinear dynamical systems. Superposition of discrete and rhythmic primitives permits the modeling of complex motor behaviors, namely locomotion in irregular terrain and obstacle avoidance. We study the effect on the amplitude and frequency of the robots’ gaits of superimposing the two motor primitives. The discrete primitive is inserted as an offset of the solution of the rhythmic primitive. We also consider three types of couplings between CPG units: synaptic, diffusive and mixed. Simulation results reveal interesting facts, in certain cases amplitude and frequency of periodic solutions, identified with hexapods’ tripod, caterpillar and metachronal gaits, remain constant. Therefore, it is possible to use these solutions to generate trajectories for the joint values of real six-legged robots, since varying the joint offset will not affect the required amplitude and frequency of the resultant trajectory nor the gait. c © 2012 European Society of Computational Methods in Sciences, Engineering and Technology