A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal information from visual and range data is used to improve obstacle detection by eliminating uncertainty in the measurements. This sensory information is then used to generate alternative trajectories which avoid collision. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.
|Original language||English (US)|
|Title of host publication||World Congress on Neural Networks|
|Publisher||Taylor and Francis|
|State||Published - Sep 10 2021|
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