Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization

Abstract

Abstract. Controlling a biped robot with a high degree of freedom to achieve stable and straight movement patterns is a complex problem. With growing computational power of computer hardware, high resolution real time simulation of such robot models has become more and more applicable. This paper presents a novel approach to generate bipedal gait for humanoid locomotion. This approach is based on modified Truncated Fourier Series (TFS) for generating angular trajectories. It is also the first time that Particle Swarm Optimization (PSO) is used to find the best angular trajectory and optimize TFS. This method has been implemented on Simulated NAO robot in Robocup 3D soccer simulation environment (rcssserver3d). To overcome inherent noise of the simulator we applied a Resampling algorithm which could lead the robustness in nondeterministic environments. Experimental results show that PSO optimizes TFS faster and better than GA to generate straighter and faster humanoid locomotion.

A Review of Gait Optimization Based on Evolutionary Computation

“…For instance, two different gait definition methods, a finite state machine based on the joint angles of the robot legs, and an Elman's recurrent neural network were studied [80], and the performance of the neural controller was reported superior (more stable, better displacement) for a simulated legged robot navigating on an irregular surface. TFS has also been reported to be a good gait representation approach and has been mentioned above [76].…”

“…These also search for the global optimum of the cost function without using the differential information of a given cost function. Particle Swarm Optimization (PSO) can be used to optimize the stable and straight movement patterns (gaits) of a humanoid robot with the control signals of the joint angles produced by a Truncated Fourier Series (TFS) [75,76]. It is reported that PSO optimized TFS significantly faster and better than GA to generate straighter and faster humanoid locomotion because PSO bypassed a local minimum that GA was caught in [76].…”

“…Particle Swarm Optimization (PSO) can be used to optimize the stable and straight movement patterns (gaits) of a humanoid robot with the control signals of the joint angles produced by a Truncated Fourier Series (TFS) [75,76]. It is reported that PSO optimized TFS significantly faster and better than GA to generate straighter and faster humanoid locomotion because PSO bypassed a local minimum that GA was caught in [76]. The authors therefore concluded that PSO is better than GA as a learning method for the gait optimization problem in a non-deterministic environment.…”

“…For example, TFS is reported to be a good gait representation approach that can generate suitable angular trajectories for controlling bipedal locomotion. This is because it does not require inverse kinematics, and stable gaits with different step lengths and stride frequencies can be readily generated by changing the value of only one parameter in the TFS [76].…”

Biped Robot Walking using a Combination of Truncated Fourier Series and GALA (Genetic Algorithm parameters adaption using Learning Automata)

“…Many researchers have focused on this issue and a lot of approaches have been presented. There are two major approaches in bipedal walking researches; model-based and model-free approaches [2,3]. In model-based approach, the designer first derives the model of the robot and then builds a controller for the model.…”