| dc.description.abstract | Personal service robots are designed to assist or entertain people in
domestic environments and expected to engage in social-human interactions;
therefore, they are gaining more and more attentions from many different fields.
A robot that can imitate human actions can be regarded as the first step for
interacting with humans from the viewpoint of actions. This dissertation presents
a neural-network-based imitation mechanism for teaching a robot to imitate
human actions. The proposed mechanism involves in the following three steps: 1)
the generation of basic human motion units, 2) the mapping between basic
human motion units and robot joint motor angles, and 3) the construction of a
NN-based controller.
First of all, we collected several human motion sequences consisted of
many different human activities and then used a clustering algorithm to cluster
the collected human actions into a set of basic human motion units. Since the
number of basic human motion units is unknown, we decided to adopt the
self-organized feature map (SOM) as the clustering tool to generate basic human
motion units. Secondly, for each basic human action unit, we need to find a
combination of robot joint motor angles to make the robot pose be similar to the
corresponding human pose. The problem can be regarded as an optimization
problem of which goal is to find an optimized solution (i.e., the best
combination of robot joint motor angles) in a multi-dimensional space. The
complexity of the optimization problem greatly increases as the number of robot
joint motors. To provide a good solution to the optimization problem, this
dissertation also proposes the new optimal algorithm called dove swarm
optimization (DSO), which is motivated by the doves’ foraging behavior. The
proposed DSO is adopted to affectively find the best combination of robot joint
motor angles corresponding to each basic human motion unit. In the third step,
the data set generated in the previous step is adopted as the training data set to
construct a NN-based controller. From our simulations, we found that controller
performance achieved by the multilayer perceptrons (MLP) outperformed the
radial basis function network (RBFN); therefore, we decided to adopt the MLP
to construct the NN-based controller.
The proposed mechanism was compared with the most straightforward
linear mapping method based on the root mean squared error and computational
time. In simulation results, we found that the proposed imitation mechanism
could promote the performance about 10% on average. The worst one hundred
basic human actions achieved by the linear mapping method, the imitation
performance could be improved to 13% by our mechanism. As for the best one
hundred basic human actions achieved by the linear mapping method, our
imitation mechanism did not clearly improve the imitation performance. | en_US |