Perception-driven sparse graphs for optimal motion planning
This addresses the challenge for fast-moving agile aerial robots with constrained computational platforms and visual sensors, where traditional map-based planning is inefficient, though it appears incremental as it builds on existing planning and perception methods.
The paper tackles the problem of motion planning when dense maps are unavailable or computationally expensive to construct, proposing an algorithm that couples perception and planning to iteratively switch between these processes, resulting in trajectories that are provably optimal with respect to the full environment while using only a fraction of sensing data.
Most existing motion planning algorithms assume that a map (of some quality) is fully determined prior to generating a motion plan. In many emerging applications of robotics, e.g., fast-moving agile aerial robots with constrained embedded computational platforms and visual sensors, dense maps of the world are not immediately available, and they are computationally expensive to construct. We propose a new algorithm for generating plan graphs which couples the perception and motion planning processes for computational efficiency. In a nutshell, the proposed algorithm iteratively switches between the planning sub-problem and the mapping sub-problem, each updating based on the other until a valid trajectory is found. The resulting trajectory retains a provable property of providing an optimal trajectory with respect to the full (unmapped) environment, while utilizing only a fraction of the sensing data in computational experiments.