Abstract: The unmanned system in interplanetary exploration has the characteristics of limited storage, computing power, energy and so on. The perception, localization and decision making algorithms based on deep neural network can effectively improve the intelligence level, but these algorithms generally require huge computing power, which is difficult to be directly applied to unmanned systems. Therefore, this paper reviews the existing lightweight methods including pruning and quantization, and makes a quantitative analysis on public dataset. Furthermore, this paper proposes pruning and quantization solutions, establishes a lightweight computing software StarLight, realizes rapid lightweight and evaluation of deep neural network, and solves the problem that the deep model is difficult to be directly applied to resource constrained systems. Finally, based on StarLight, various models used in the Mars rover are compressed, and deployed in the embedded platform; under the premise of ensuring performance, the power≤15 W, CPU frequency≤1.2 GHz and storage≤1 TB. Experiments show that the software can meet the lightweight requirements of resource constrained systems, and builds a foundation for further improving the intelligent level of unmanned systems for interplanetary exploration.

Key words: unmanned system, deep neural network, lightweight computing