Trajectory Planning and Control of a Quadrotor Choreography for Real-Time Artist-in-the-Loop Performances
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".