论文标题
使用深度学习用于空间应用的多机构运动计划
Multi-Agent Motion Planning using Deep Learning for Space Applications
论文作者
论文摘要
最先进的运动计划者无法扩展到大量系统。多种试剂的运动计划是NP（非确定性多项式时间）硬问题，因此计算时间随着每次添加代理而成倍增加。这种计算需求是运动计划者对未来NASA任务涉及太空车辆的未来任务的主要绊脚石。我们应用了一个深层神经网络，将计算要求的数学运动计划问题转变为基于深度学习的数值问题。我们显示，使用基于多种代理的几种2D和3D系统中的基于深度学习的数值模型可以准确地复制最佳运动轨迹。基于深度学习的数值模型表明，与数学模型对应的计划生成的速度快1000倍。
State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.
