Efficient Path Planning and Tracking for Multi-Modal Legged-Aerial Locomotion Using Integrated Probabilistic Road Maps (PRM) and Reference Governors (RG)

Kavli Affiliate: Morteza Gharib

| First 5 Authors: Eric Sihite, Benjamin Mottis, Paul Ghanem, Alireza Ramezani, Morteza Gharib

| Summary:

There have been several successful implementations of bio-inspired legged
robots that can trot, walk, and hop robustly even in the presence of
significant unplanned disturbances. Despite all of these accomplishments,
practical control and high-level decision-making algorithms in multi-modal
legged systems are overlooked. In nature, animals such as birds impressively
showcase multiple modes of mobility including legged and aerial locomotion.
They are capable of performing robust locomotion over large walls, tight
spaces, and can recover from unpredictable situations such as sudden gusts or
slippery surfaces. Inspired by these animals’ versatility and ability to
combine legged and aerial mobility to negotiate their environment, our main
goal is to design and control legged robots that integrate two completely
different forms of locomotion, ground and aerial mobility, in a single
platform. Our robot, the Husky Carbon, is being developed to integrate aerial
and legged locomotion and to transform between legged and aerial mobility. This
work utilizes a Reference Governor (RG) based on low-level control of Husky’s
dynamical model to maintain the efficiency of legged locomotion, uses
Probabilistic Road Maps (PRM) and 3D A* algorithms to generate an optimal path
based on the energetic cost of transport for legged and aerial mobility

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