Enabling Bidirectional Thrust for Aggressive and Inverted Quadrotor Flight
Bibliographic record
Abstract
Quadrotors have become the most common and highly popularized small aerial vehicles among robotics researchers, consumers and commercial users. Traditionally, these platforms have been designed with each of the four propellers to spin in the designated direction (two clockwise and two counter-clockwise) to produce unidirectional thrust-this allows the vehicle to oppose gravity when in its nominal hover orientation. In this paper, we present a quadrotor which is capable of bidirectional thrust actuation: it is generated by reversing the direction of the driving motors and hence propeller spins. This configuration is motivated by the desire to increase the vehicle's agility, as well as to imbue it with functionalities not available to the standard unidirectional thrust platforms. We present the dynamics model of a quadrotor with bidirectional thrust, the controller adapted to this configuration, and details of the implementation of the bidirectional capability in hardware and software, using the state-of-the-art Pixhawk micro-controller and PX4 flight stack. The transient thrust characteristics of a symmetric propeller are experimentally determined, highlighting the maximum rate of change and dead-zone specific to bidirectional thrust. Results starting from simulation, to hardware-in-the-loop testing, to experiments conducted with an outdoor platform are presented for a half flip maneuver, demonstrating the performance and upside down hovering of the vehicle.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".