首页|Findings in the Area of Robotics Reported from Syracuse University (Energy-optim al Asymmetrical Gait Selection for Quadrupedal Robots)

Findings in the Area of Robotics Reported from Syracuse University (Energy-optim al Asymmetrical Gait Selection for Quadrupedal Robots)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting from Syracuse, New York, by NewsRx journalis ts, research stated, "Symmetrical gaits, such as trotting, are commonly employed in quadrupedal robots for their simplicity and stability. However, the potentia l of asymmetrical gaits, such as bounding and galloping-which are prevalent in t heir natural counterparts at high speeds or over long distances-is less clear in the design of locomotion controllers for legged machines." Financial support for this research came from Syracuse University. The news correspondents obtained a quote from the research from Syracuse Univers ity, "This study systematically examines five distinct asymmetrical quadrupedal gaits on a legged robot, aiming to uncover the fundamental differences in footfa ll sequences and the consequent energetics across a broad range of speeds. Utili zing a full-body model of a quadrupedal robot (Unitree A1), we developed a hybri d system for each gait, incorporating the desired footfall sequence and rigid im pacts. To identify the most energyoptimal gait, we applied optimal control meth ods, framing it as a trajectory optimization problem with specific constraints a nd a work-based cost of transport as an objective function. Our results show that, in the context of asymmetrical gaits, when minimizing cost of transport acros s the entire stride, the front leg pair primarily propels the system forward, wh ile the rear leg pair acts more like an inverted pendulum, contributing signific antly less to the energetic output."

SyracuseNew YorkUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsSyracuse University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.4)