首页|New Findings from Northwestern Polytechnic University Describe Advances in Robot ics (Reconciling Conflicting Intents: Bidirectional Trust-based Variable Autonom y for Mobile Robots)

New Findings from Northwestern Polytechnic University Describe Advances in Robot ics (Reconciling Conflicting Intents: Bidirectional Trust-based Variable Autonom y for Mobile Robots)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “In the realm of semi-auton omous mobile robots designed for remote operation with humans, current variable autonomy approaches struggle to reconcile conflicting intents while ensuring com pliance, autonomy, and safety. To address this challenge, we propose a bidirecti onal trust-based variable autonomy (BTVA) control approach.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Northwestern Polytechni c University, “By incorporating diverse trust factors and leveraging Kalman filt ering techniques, we establish a core abstraction layer to construct the state-s pace model of bidirectional computational trust. This bidirectional trust is int egrated into the variable autonomy control loop. Real-time modulation of the deg ree of automation is achieved through variable weight receding horizon optimizat ion. Through a within-group experimental study with twenty participants in a sem i-autonomous navigation task, we validate the effectiveness of our method in goa l transfer and assisted teleoperation. Statistical analysis reveals that our met hod achieves a balance between rapid response and trajectory smoothness.”

Xi’anPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsNorthwestern Pol ytechnic University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.3)