Uncertainty Aware Planner

Uncertainty-aware planners aim to improve the robustness and efficiency of robot navigation by explicitly incorporating sensor uncertainty and environmental unpredictability into path planning algorithms. Current research focuses on developing planners that leverage probabilistic models, such as stochastic occupancy grid maps and variational autoencoders, to predict future environmental states and propagate uncertainty through the planning process. This leads to more reliable and adaptable robot behavior in dynamic and partially observable environments, with applications ranging from multi-agent coordination to autonomous exploration and precision tasks like perching.