GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

摘要

Long-range locomotion planning is an important problem for the deployment of legged robots to real scenarios. Current methods used for legged locomotion planning often do not exploit the flexibility of legged robots, and do not scale well with environment size. In this letter we propose the use of navigation meshes for deployment in large-scale multi-floor sites. We leverage this representation to improve long-term locomotion plans in terms of success rates, path costs and reasoning about which gait-controller to use when. We show that NavMeshes have higher planning success rates than sampling-based planners, but are 400x faster to construct and at least 100x faster to plan with. The performance gap further increases when considering multi-floor environments. We present both a procedure for building controller-aware NavMeshes and a full navigation system that adapts to changes to the environment. We demonstrate the capabilities of the system in simulation experiments and in field trials at a real-world oil rig facility.