Abstract
AbstractBipedal robots by their nature show both hybrid and underactuated system features which are not stable and controllable at every point of joint space. They are only controllable on certain fixed equilibrium points and some trajectories that are periodically stable between these points. Therefore, it is crucial to determine the trajectory in the control of walking robots. However, trajectory optimization causes a heavy computational load. Conventional methods to reduce the computational load weaken the optimization accuracy. As a solution, a variable time interval trajectory optimization method is proposed. In this study, optimization accuracy can be increased without additional computational time. Moreover, a five-link planar biped walking robot is designed, produced, and the dynamic walking is controlled with the proposed method. Finally, cost of transport (CoT) values are calculated and compared with other methods in the literature to reveal the contribution of the study. According to comparisons, the proposed method increases the optimization accuracy and decreases the CoT value.
Highlights
In human-designed environments, legged robots have the advantage of movement compared to wheeled robots
The controller that will ensure the dynamic walking of the biped robot developed as a test platform has been studied
Dynamic walking is simulated according to the robot mathematical model in the Matlab-Simulink environment with a fixed sampling interval of 400 us
Summary
In human-designed environments, legged robots have the advantage of movement compared to wheeled robots. Methods are proposed in different disciplines to shorten the processing time of trajectory optimization [15]. Hybrid zero dynamic (HZD) is one of the dynamic gait control methods that is applied in underactuated biped robots [4, 16, 17]. The fact that the trajectories depend on this increasing function instead of time ensures that the motion space of the robot is limited by virtual constraints [18] In this way, stable walking depending on desired trajectories can be achieved.
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