Applications for unmanned aerial and ground vehicles requiring autonomous navigation in unknown, cluttered, and dynamically changing environments are increasing in fields such as transportation, delivery, agriculture, environmental monitoring, and construction. To achieve safe, resilient, and self-improving autonomous navigation, this project focuses on the design of adaptive online environment understanding that guarantees stable and collision-free operation in challenging conditions. The proposed research is important because current practices rely on prior maps or hand-crafted online mapping that attempt to capture the whole environment, even if parts are irrelevant for specific navigation tasks. This increases memory and computation requirements, spreads the effects of noise, and makes current approaches brittle, particularly in conditions involving dynamic obstacles, unreliable localization, or illumination variation.
The proposal offers two technical innovations to achieve safe autonomous navigation. First, it develops a learnable neural map based on 3-D convolution over hierarchical (octree) partitioning of space to extract navigation-specific features and on differentiable memory to infer long-term dependence among the features. The neural map parameters are trained from navigation experience not to produce accurate maps but to quantify the collision probabilities of intended motion trajectories accurately. The second innovation is a Lyapunov-theoretic control approach that uses the total energy of an autonomous system with respect to a virtual kinematic system (that can stop immediately) to derive conditions that guarantee stable and collision-free tracking of the trajectories proposed by the neural network. The proposed learnable neural map significantly increases the robustness of collision prediction, while the Lyapunov-theoretic control guarantees stable and safe navigation in new, unpredictable, and cluttered environments.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
