Learning Navigational Maps by Observing Human Motion Patterns
Observing human motion patterns is informative for social robots that share the environment with people. This paper presents a methodology to allow a robot to navigate in a complex environment by observing pedestrian positional traces. A continuous probabilistic function is determined using Gaussian process learning and used to infer the direction a robot should take in different parts of the environment. The approach learns and filters noise in the data producing a smooth underlying function that yields more natural movements. Our method combines prior conventional planning strategies with most probable trajectories followed by people in a principled statistical manner, and adapts itself online as more observations become available. The use of learning methods are automatic and require minimal tuning as compared to potential fields or spline function regression. This approach is demonstrated testing in cluttered office and open forum environments using laser and vision sensing modalities. It yields paths that are similar to the expected human behaviour without any a priori knowledge of the environment or explicit programming.
Simon T. O’Callaghan Surya P. N. Singh Alen Alempijevic Fabio T. Ramos
Australian Centre for Field Robotics,University of Sydney,J04,NSW 2006 Australia Mechatronics & Intelligent Systems Group,University of Technology Sydney,NSW 2007
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4333-4340
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）