Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion
This addresses energy-efficient motion planning for robots with hybrid locomotion, enabling complex tasks in diverse environments, but it appears incremental as it builds on existing approximate dynamic programming methods.
The paper tackled the problem of planning multi-modal locomotion trajectories for robots by formulating it as a shortest-path search with edge costs approximated from offline optimizations, demonstrating practicality on hybrid systems like an amphibious robot and flying-driving drone.
Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach.