Abstract
The existing mechanism parameter optimization (MPO) method of parallel mechanisms only considers the workspace size and ignores contribution of each configuration’s performance. So a novel MPO method is proposed for our serial-parallel mechanism platform, which is used in stability training of legged robots. Regarding the platform’s parallel mechanism part, a 4-PSS/PS parallel mechanism, two object functions and three constraint conditions are defined to establish the MPO model. The first object function uses critical motion indexes of the moving platform. The second one uses derivative function of the defined disturbance Lagrange function. After analyzing stability-training requirements of five existing legged robots, requirements of the platform’s motion capability are given out. Regarding each proposed object function separately, the MPO model is solved by the particle swarm optimization (PSO) algorithm. Valid workspace boundaries corresponding to the optimization results are solved by a numerical method. The overall optimal solution is determined based on volume of the valid workspace. It is revealed that the two object functions result in similar optimization solutions, which shows that the proposed object functions can reflect the stability-training ability consistently. This paper proposes and verifies the established MPO model, which considers both the workspace size and configurations’ performance evaluation.
Highlights
Legged robots influenced by the environment disturbance may be unstable, so balancing control is needed to obtain the self-stabilizing ability
In order to reduce the dependency to the dynamics model, intelligence algorithms were applied to design intelligence stabilizers, such as: the fuzzy algorithm [9, 10], the artificial neural networks [11], the reinforcement learning algorithm [12, 13], and so on
By integration in the workspace, mechanism parameter optimization (MPO) object function is proposed for each evaluation index, which considers both workspace’s size and the training performance of each mechanism configuration
Summary
Legged robots influenced by the environment disturbance may be unstable, so balancing control is needed to obtain the self-stabilizing ability. (2018) 31:50 it is hard to get the self-stabilizing ability with strong robustness, neither can get the animal-like global selfstabilizing ability, which keeps balancing against arbitrary disturbances within the driving ability limitation. Toward this problem, the authors designed a serialparallel mechanism platform (Figure 1(a)) with 6 degreeof-freedoms (DOFs) [15, 16], and proposed the idea of obtaining robot’s self-stabilizing ability by actively training (Figure 1(b)). Regarding to the MPO issue, dexterity evaluation indexes were proposed based on the Jacobian matrix of the Moving platform
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