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Advanced Autonomy on a Low-Cost Educational Drone Platform

Year: 2019
Type: conference-paper
Abstract: PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignments and projects. Existing educational robots do not have significant autonomous capabilities, such as high-level planning and mapping. We present a hardware and software framework for an autonomous aerial robot, in which all software for autonomy can run onboard the drone, implemented in Python. We present an Unscented Kalman Filter (UKF) for accurate state estimation. Next, we present an implementation of Monte Carlo (MC) Localization and FastSLAM for Simultaneous Localization and Mapping (SLAM). The performance of UKF, localization, and SLAM is tested and compared to ground truth, provided by a motion-capture system. Our evaluation demonstrates that our autonomous educational framework runs quickly and accurately on a Raspberry Pi in Python, making it ideal for use in educational settings.
DOI: 10.1109/iros40897.2019.8967595
Authors: Luke Eller, Théo Guérin, Baichuan Huang, Garrett Warren, Sophie Yang, Josh Roy, Stefanie Tellex
Institution: Brown University
Language: English
Open Access status: green
Award: THE AIM OF THIS PROPOSAL IS TO TEST THE HYPOTHESIS THAT A SYSTEM CAN INCREASE SPEED AND ACCURACY AT INFERRING HUMAN INTENTIONS AND INCREASE THE NUMBER OF ROBOTS A SINGLE ASTRONAUT CAN SUPERVISE BY INFERRING A PERSON'S MENTAL STATE FROM THEIR LANGUAGE UTTERANCES ACTIVELY ASKING QUESTIONS WHEN IT IS CONFUSED DRIVEN BY A FORMAL MODEL OF HRI THAT LEARNS A HIERARCHICAL REPRESENTATION FOR PLANNING UNDER UNCERTAINTY CALLED THE HUMAN-ROBOT COLLABORATIVE POMDP. THIS INFERENCE WILL ENABLE A PERSON TO EFFECTIVELY COMMUNICATE COMPLEX REQUIREMENTS AND TASKS CONSTRAINTS TO THE PERSON AT VERY ABSTRACT LEVELS (E.G. ``INSPECT THE ISS'') AND AT VERY SPECIFIC LEVELS (E.G. ``MOVE LEFT SIX INCHES '') AS WELL AS ASKING TARGETED QUESTIONS AT DIFFERENT LEVELS OF ABSTRACTION (E.G. ``DO YOU MEAN THE PHILIPS SCREWDRIVER OR THE FLAT HEAD?''). THE END GOAL OF THIS PROPOSAL IS TO ACHIEVE SEAMLESS HUMAN-ROBOT COOPERATION ON COMPLEX TASKS APPROACHING THE EASE AND ACCURACY OF HUMAN-HUMAN COLLABORATION. HUMANS COMMUNICATING WITH OTHER HUMANS USING LANGUAGE TO EXPRESS VERY ABSTRACT GOALS AS WELL AS VERY LOW-LEVEL CONCRETE COMMANDS. THIS USE OF LANGUAGE ENABLES HIGH LEVELS OF AUTONOMY BUT ALSO SUPPORTS FLEXIBLE AND FLUID CORRECTIONS AND REPLANNING. ROBOTS THAT CAN USE FLUID LANGUAGE AT MULTIPLE LEVELS OF ABSTRACTION CAN FLEXIBLY RESPOND TO A PERSON'S REQUESTS. THE ULTIMATE IMPACT IS A WORLD WHERE ROBOTS ACTIVELY INTERPRET A PERSON'S INSTRUCTIONS ASKING QUESTIONS WHEN THEY ARE CONFUSED AND ASKING FOR HELP WHEN THEY ENCOUNTER A PROBLEM. THIS PROPOSAL WILL ASSESS PERFORMANCE USING TWO TASKS: NATURAL LANGUAGE CONTROL OF AN AR DRONE UAV (AS A MODEL FOR ASTROBEE) AND NATURAL LANGUAGE-BASED COLLABORATIVE ASSEMBLY USING BAXTER (AS A MODEL FOR ROBONAUT). THESE TASKS WILL ASSESS THE ABILITY OF THE FRAMEWORK TO FACILITATE IMPROVED HUMAN-ROBOT COLLABORATION USING LANGUAGE-BASED INTERFACES AND DECISION-THEORETIC PLANNING.
OpenAlex ID: W2979705751
FWCI: 1.813
Cites: 12
Cited by: 7
Related to: 10