Online Gait Generation Research Articles

Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance

The development of lower limb exoskeletons has seen significant interest in recent times. Two types of them are more used that cover two types of needs: gait rehabilitation and human locomotion assistance. An essential subject in controlling the latter kind is trajectory generation, in which there are still challenges. For online controlling of the exoskeleton, gait parameters must have the ability to change at any moment during walking, taking into account human intention and particular conditions. In this paper, an online gait generation method is provided that is suitable for different walking modes. For this purpose, three trajectory generator blocks are proposed. The first block is for the center of mass (CoM) in the double support phase, where the trajectory is generated to make the patient feel more comfortable. The second block is for the support leg in the single support phase. The trajectory is generated using the center of pressure (CoP) criterion to secure backward balance and reduce the forces applied to the arms. The last block is for the swing leg in the single support phase, where a cost function is proposed to minimize the torques of the motors. The performance analysis of the proposed trajectory generator blocks was evaluated, and walking patterns were examined via simulations. Finally, three experimental tests were implemented with a healthy subject wearing Exoped® exoskeleton on level-ground with an obstacle, and stairs.

Robust model predictive control of biped robots with adaptive on-line gait generation

In this paper, an on-line gait control scheme is proposed for the biped robots for walking up and down the stairs. In the proposed strategy, the nonlinear model predictive control approach is used for the trajectory planning and as well as for the control of the robot. The motion of the robot is expressed in the form of a cost function and some constraints that are related to the stable walking of the robot. The main feature of this method is that it does not need any off-line trajectory planning and the walking gait is formulated such that the environmental and stability constraints of the robot are satisfied. This on-line trajectory planning gives the important ability to the robot to adjust its gait lengths. In this way, the robot is able to ascend and descend the stairs without knowing the height and depth of the stairs in advance. In the control algorithm, the Radial-Basis Function (RBF) neural network with on-line training method is used to model the behavior of the robot over the prediction horizon. The stability analysis of the closed-loop system is performed using the Lyapunov method as well as the Poincare map. The proposed method is applied to a 5-DOF biped robot in the sagittal plane. The simulation results show effectiveness of the proposed method.

Development of a new spinning gait for a planar snake robot using central pattern generators

In this paper, we first present dynamic equation of n-link snake robot using Lagrange's method in a simplified matrix form and verify them experimentally. Next, we introduce a new locomotion mode called spinning gait. Central pattern generators (CPGs) are used for online gait generation. To realize spinning gait, genetic algorithm is used to find optimal CPG network parameters. We illustrate both theoretically, using derived robot dynamics and experimentally that the CPG-based online gait generation method allows continuous and rather smooth transitions between gaits. Lastly, we present an application where the snake robot is guided from an initial to final position while avoiding obstacles by changing CPG parameters.

On the Suitability of Different Online Gait Generation Methods for Pre‐Defined Footsteps

AbstractDetailed dynamical modeling is the basis for simulation and model based control. In this contribution the Projection Equation is used for the modeling of a biped walking machine, resulting in the equations of motion which are needed for gait generation and verification of its stability. For biped robots one main issue is the generation of stable trajectories for the center of mass (CoM). Several different approaches based on the Zero Moment Point (ZMP) scheme have been presented in the past. Due to the complex dynamic structure of bipedal robots most of the considered algorithms use a linear inverted pendulum as a simplified model. This results in a decoupling of the ZMP equations in lateral and forward direction, but limits the trajectories to a constant height of the CoM. An extension of the well known LQR theory by future reference values has been proposed. This model based approach seems to perform quite well, but does not allow the consideration of constraints on the position of the ZMP. This limitation is removed by the use of Model Predictive Control (MPC) with inequality constraints. By extending this approach to a time invariant one the restriction to a constant height of the CoM is no longer necessary. Both methods as well as the time invariant approach for variable CoM heights have been evaluated in simulations and will be experimentally verified on a real robot soon. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

ONLINE DYNAMICALLY BALANCED ASCENDING AND DESCENDING GAIT GENERATIONS OF A BIPED ROBOT USING SOFT COMPUTING

In the present paper, two algorithms based on soft computing have been developed for dynamically balanced gait generations of a biped robot ascending and descending a staircase. The utility of the soft computing tools is best justified, when the data available for the problem to be solved are imprecise in nature, difficult to model and exhibit large-scale solution spaces. The problem of online gait generation of a biped robot exhibits such a complex phenomenon, and ultimately soft computing has become a natural choice for solving it. The gait generation problems of a biped robot have been solved using two different approaches, namely genetic-neural (GA-NN) and genetic-fuzzy (GA-FLC) systems. In GA-NN, the gait generation problem of a two-legged robot has been modeled using two modules of Neural Network (NN), whose weights are optimized offline using a Genetic Algorithm (GA), whereas in GA-FLC, the above problem is modeled utilizing two modules of Fuzzy Logic Controller (FLC) and their rule bases are optimized offline using a GA. Once optimized, the GA-NN and GA-FLC systems will be able to generate dynamically balanced gaits of the biped robot online. The performances of the two approaches are compared with respect to the Dynamic Balance Margin (DBM).