Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning
CoRR(2024)
摘要
In order to solve the problem of frequent deceleration of unmanned vehicles
when approaching obstacles, this article uses a Deep Q-Network (DQN) and its
extension, the Double Deep Q-Network (DDQN), to develop a local navigation
system that adapts to obstacles while maintaining optimal speed planning. By
integrating improved reward functions and obstacle angle determination methods,
the system demonstrates significant enhancements in maneuvering capabilities
without frequent decelerations. Experiments conducted in simulated environments
with varying obstacle densities confirm the effectiveness of the proposed
method in achieving more stable and efficient path planning.
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