Dynamic Maps for Long-Term Operation of Mobile Service Robots 论文
2005引用 296
Evolutionary Algorithms and ApplicationsReinforcement Learning in RoboticsArtificial Immune Systems Applications
摘要
This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stabilityplasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale.