Ice Robot Target Detection and Recognition Based on Reinforcement Learning Algorithm

Performing robot tasks in ice environments such as polar and high cold is a challenging problem, involving complex terrain and climatic conditions, which puts forward higher requirements on the robot's perception, planning and execution capabilities. This article studies a target detection and recognition method for ice robots based on reinforcement learning algorithms. Traditional supervised learning methods face issues such as data scarcity, environmental changes, and task complexity in ice environments. This article adopts the interaction between agents and the environment in reinforcement learning algorithms to learn the optimal action strategy. The task of object detection and recognition is modeled as a reinforcement learning problem, where the state of the agent is the image or sensor data in the environment. The action is the specific operation performed by the robot, and the reward is the accuracy or other evaluation indicators of object detection and recognition. Continuously interacting with the environment and optimizing strategies, intelligent agents gradually improve the performance of object detection and recognition. The effectiveness and superiority of this method in ice robot target detection and recognition tasks are very significant. The missed detection rate and false detection rate of ice robots based on Actor-Critic algorithm in target detection and recognition tasks are as low as 6.4% and 0.1%, respectively. The research in this article is of great significance for improving the autonomous perception and decision-making ability of ice robots in complex environments.