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Vision-Based Local Obstacle Avoidance using Obstacle Dependent Gaussian Potential Field
Dong-Sung Pae*, Ye-Ri Cho**, Yun-Kyu Lee**, Hye-Yeun Chun**, Myo-Taeg Lim* and Tae-Koo Kang
Abstract We address a vision-based local obstacle avoidance method. The proposed method is based on the Obstacle Dependent Gaussian Potential Field (ODG-PF) which is a local path planning algorithm. Basically, the Potential Field (PF) algorithm is a global path planning algorithm. Therefore, we modify the conventional PF to apply the local path planning condition which do not need any environment information. In advance, the proposed method makes the robot to avoid the obstacles according to the decision of the optimal path by itself.
Keyword Local obstacle avoidance, Obstacle Dependent Gaussian Potential Field (ODG-PF)
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