Online path planning under uncertainty

The authors deal with an online planning algorithm. The work is motivated by robot navigation and manipulation tasks in unstructured, dynamic environments. It is assumed that sensory information is incomplete and must be expanded and/or redefined by active sensing during an exploratory motion phase. Candidate targets are modeled as attractors, while obstacles are modeled as repellers. Path planning is reduced to an iterative Newton scheme that can readily adapt to changes in the environment and to new sensory information. Julia sets are used to detect and avoid chaotic convergence