LTO: Lazy Trajectory Optimization with Graph-Search Planning for High DOF Robots in Cluttered Environments
This addresses motion planning challenges for high-DOF robots in complex settings, offering incremental improvements in efficiency and optimality.
The paper tackles the high computational cost and convergence issues of trajectory optimization for high-degree-of-freedom robots in cluttered environments by proposing Lazy Trajectory Optimization (LTO), which unifies local short-horizon optimization with global graph-search planning to achieve improved runtime and reliability, as demonstrated on robots with 2 and 21 DOFs.
Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can balance both solution cost and planning time. Since LTO solves many nearly identical TO in a roadmap, it can provide an informed warm-start for TO to accelerate the planning process. We also present proofs of the computational complexity and optimality of LTO. Finally, we demonstrate LTO's performance on motion planning problems for a 2 DOF free-flying robot and a 21 DOF legged robot, showing that LTO outperforms existing algorithms in terms of its runtime and reliability.