Efficient Path Planning and Tracking for Multi-Modal Legged-Aerial Locomotion Using Integrated Probabilistic Road Maps (PRM) and Reference Governors (RG)
Eric Sihite, Benjamin Mottis, Paul Ghanem, Alireza Ramezani, Morteza, Gharib

TL;DR
This paper presents an integrated approach combining probabilistic road maps and reference governors to enable efficient path planning and transition control for a multi-modal legged-aerial robot, inspired by animal versatility.
Contribution
It introduces a novel control framework that combines PRM, 3D A* algorithms, and reference governors for seamless multi-modal locomotion in robots.
Findings
Successful path planning with energy optimization for legged and aerial modes.
Effective transition control between ground and aerial mobility.
Enhanced robustness in complex environments.
Abstract
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.…
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Taxonomy
TopicsRobotic Locomotion and Control · Soil Mechanics and Vehicle Dynamics · Hemiptera Insect Studies
