This paper describes the application of Particle Swarm Optimization (PSO) for gait optimization on a humanoid robot. The biped gait is modeled by a number of parameterizable trajectories. To achieve omni-directional walking, different sets of gait parameters are optimized for specific walk directions and interpolated later. By using a fitness test based on an acceleration walk, the optimized se...

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance metric, such as robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. Instead, we propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, ...

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parameterization, finding nearoptimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and t...

In this paper, humanoid gait generation is formulated as a multi-objective optimization problem with multiconstraint based on probability distribution models. Under this framework, an estimation of distribution algorithm (EDA) based gait optimization approach has been developed to speed up searching in high dimensional coupling space constructed by the permutation of optimization parameters to ...

Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not expli...

Physical parameters of the constituent modules and gait parameters affect the overall performance of snake-inspired robots. Hence a system-level optimization model needs to concurrently optimize the module parameters and the gait. Incorporating a physics-based model of rectilinear gaits in the system-level optimization model is a computationally challenging problem. This paper presents a case s...

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The presented paper addresses a comparison bet...

This paper presents the methodology used for finding the optimal set of foot trajectories for a quadruped robot using manual tuning and multiobjective genetic algorithm optimization that provide energy efficient and fast locomotion. Manual trajectory tuning is used to obtain initial set of trajectories for the multiobjective GA optimization. The multiobjective optimization evaluates the energy ...

This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot ascending and descending some staircases. Both its power consumption as well as dynamic balance margin depends on a few common design parameters. The biped robot should have a maximum dynamic balance margin but at the expense of minimum power. Thus, a conflicting relationship exists between these two obje...

the main objective of this article is to optimize the walking pattern of a 2d humanoid robot with heel-off and toe-off motions in order to minimize the energy consumption and maximize the stability margin. to this end, at first, a gait planning method is introduced based on the ankle and hip joint position trajectories. then, using these trajectories and the inverse kinematics, the position tra...