Trajectory Planning and Control of a Quadrotor Choreography for Real-Time Artist-in-the-Loop Performances
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a systematic methodology for the guidance, control, and navigation of a quadrotor to perform a choreographed dance in real-time as a function of, and interacting with, the music performed by an artist-in-the-loop. This methodology allows for a real-time interaction with improvized music by an artist based on the pitch of the acoustic signal being played without prior knowledge of the music. The four main components of a human choreography (namely, the notions of space, shape, time and structure) are analyzed and mathematically formulated for a robotic performance. A new approach for mapping music features to trajectory parameters is proposed, as well as the design of a trajectory shaping filter based on two coefficients that are set in real-time by an artist through a MIDI foot-pedal board. The proposed approach maps motion parameters and the music to trajectory motifs that are then switched in harmony with the chord structure. The overall system is validated in a hardware-in-the-loop simulation where the hardware will consist of the musical instrument and the foot pedals. In the simulation, the trajectory generator system is inverted to generate a sequence of music pitches from the actual trajectory of the quadrotor. The music generated by the quadrotor is then played back to the musician allowing for real-time interaction. The simulation results show that the proposed methodology yields an effective performance for a quadrotor choreography based on the real-time interaction with a musician. The proposed system was successfully used by an artist as can be seen in a video link to the work described in this paper and listed in the conclusions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it