Improving the Reliability of Pick-and-Place With Aerial Vehicles Through Fault-Tolerant Software and a Custom Magnetic End-Effector

Aerial manipulation is an emerging field in robotics with various potential applications such as transport and delivery, agriculture, and, infrastructure inspection. To deploy aerial vehicles in the real world, the safety and reliability of these systems is paramount. Motivated by the need for safet...

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Vydáno v:IEEE robotics and automation letters Ročník 6; číslo 4; s. 7501 - 7508
Hlavní autoři: Garimella, Gowtham, Sheckells, Matthew, Kim, Soowon, Baraban, Gabriel, Kobilarov, Marin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Bias
Contact force
Distribution centers
End effectors
factory automation
Failure modes
Fault tolerance
Hardware
Infrastructure
Inspection
Manipulators
Planning
Reliability
Robotics
Safety
scheduling and coordination
Software
software architecture for robotic and automation
Software reliability
Task analysis
Trajectory
ISSN:2377-3766, 2377-3766
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Abstract Aerial manipulation is an emerging field in robotics with various potential applications such as transport and delivery, agriculture, and, infrastructure inspection. To deploy aerial vehicles in the real world, the safety and reliability of these systems is paramount. Motivated by the need for safety and reliability, this work proposes a software framework that has built-in robustness to algorithmic failures and hardware faults. The framework allows users to build complex applications while reasoning about faults that can happen at different stages of an aerial manipulation task and specifying fallback actions to return to normal operating mode. The aerial manipulator is further endowed with a magnetic gripper that can handle positional errors arising from perception and control uncertainties. We also introduce a bias estimator for measuring the contact forces and sensor bias. We demonstrate how the estimator can be used to detect either completion or failures across several tasks. We demonstrate the reliability of the proposed framework on two tasks: package sorting task (e.g. as might be used in a distribution center) and sensor placement task (for infrastructure inspection). We show different failure modes that can occur and how our aerial manipulation system recovers from them.
AbstractList Aerial manipulation is an emerging field in robotics with various potential applications such as transport and delivery, agriculture, and, infrastructure inspection. To deploy aerial vehicles in the real world, the safety and reliability of these systems is paramount. Motivated by the need for safety and reliability, this work proposes a software framework that has built-in robustness to algorithmic failures and hardware faults. The framework allows users to build complex applications while reasoning about faults that can happen at different stages of an aerial manipulation task and specifying fallback actions to return to normal operating mode. The aerial manipulator is further endowed with a magnetic gripper that can handle positional errors arising from perception and control uncertainties. We also introduce a bias estimator for measuring the contact forces and sensor bias. We demonstrate how the estimator can be used to detect either completion or failures across several tasks. We demonstrate the reliability of the proposed framework on two tasks: package sorting task (e.g. as might be used in a distribution center) and sensor placement task (for infrastructure inspection). We show different failure modes that can occur and how our aerial manipulation system recovers from them.
Author Sheckells, Matthew
Baraban, Gabriel
Garimella, Gowtham
Kim, Soowon
Kobilarov, Marin
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SubjectTerms Bias
Contact force
Distribution centers
End effectors
factory automation
Failure modes
Fault tolerance
Hardware
Infrastructure
Inspection
Manipulators
Planning
Reliability
Robotics
Safety
scheduling and coordination
Software
software architecture for robotic and automation
Software reliability
Task analysis
Trajectory
Title Improving the Reliability of Pick-and-Place With Aerial Vehicles Through Fault-Tolerant Software and a Custom Magnetic End-Effector
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