A reinforcement learning based construction material supply strategy
  using robotic crane and computer vision for building reconstruction after an
  earthquake
Yifei Xiao T. Y. Yang Xiao Pan Fan Xie Zhongwei Chen
After an earthquake, it is particularly important to provide the necessary
resources on site because a large number of infrastructures need to be repaired
or newly constructed. Due to the complex construction environment after the
disaster, there are potential safety hazards for human labors working in this
environment. With the advancement of robotic technology and artificial
intelligent (AI) algorithms, smart robotic technology is the potential solution
to provide construction resources after an earthquake. In this paper, the
robotic crane with advanced AI algorithms is proposed to provide resources for
infrastructure reconstruction after an earthquake. The proximal policy
optimization (PPO), a reinforcement learning (RL) algorithm, is implemented for
3D lift path planning when transporting the construction materials. The state
and reward function are designed in detail for RL model training. Two models
are trained through a loading task in different environments by using PPO
algorithm, one considering the influence of obstacles and the other not
considering obstacles. Then, the two trained models are compared and evaluated
through an unloading task and a loading task in simulation environments. For
each task, two different cases are considered. One is that there is no obstacle
between the initial position where the construction material is lifted and the
target position, and the other is that there are obstacles between the initial
position and the target position. The results show that the model that
considering the obstacles during training can generate proper actions for the
robotic crane to execute so that the crane can automatically transport the
construction materials to the desired location with swing suppression, short
time consumption and collision avoidance.