Perception-Aware Human-Assisted Navigation of Mobile Robots on Persistent Trajectories

We propose a novel shared control and active perception framework combining the skills of a human operator in accomplishing complex tasks with the capabilities of a mobile robot in autonomously maximizing the information acquired by the onboard sensors for improving its state estimation. The human o...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 5; H. 3; S. 4711 - 4718
Hauptverfasser: Cognetti, Marco, Aggravi, Marco, Pacchierotti, Claudio, Salaris, Paolo, Giordano, Paolo Robuffo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Active control
Computer Science
Feedback
Human-centered robotics
Maximization
Mobile robots
Optimization
optimization and optimal control
Perception
reactive and sensor-based planning
Robot sensing systems
Robotics
Robots
Splines (mathematics)
State estimation
Task analysis
Task complexity
Trajectory
Reactive and Sensor- Based Planning
Human-Centered Robotics
Optimization and Optimal Control
ISSN:2377-3766, 2377-3766
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Zusammenfassung:We propose a novel shared control and active perception framework combining the skills of a human operator in accomplishing complex tasks with the capabilities of a mobile robot in autonomously maximizing the information acquired by the onboard sensors for improving its state estimation. The human operator modifies at runtime some suitable properties of a persistent cyclic path followed by the robot so as to achieve the given task (e.g., explore an environment). At the same time, the path is concurrently adjusted by the robot with the aim of maximizing the collected information. This combined behavior enables the human operator to control the high-level task of the robot while the latter autonomously improves its state estimation. The user's commands are included in a task priority framework together with other relevant constraints, while the quality of the acquired information is measured by the Shatten norm of the Constructibility Gramian. The user is also provided with guidance feedback pointing in the direction that would maximize this information metric. We evaluated the proposed approach in two human subject studies, testing the effectiveness of including the Constructibility Gramian into the task priority framework as well as the viability of providing either visual or haptic feedback to convey this information metric.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3003882