Bio-inspired walking: A FPGA multicore system for a legged robot
Bibliographic record
Abstract
Previous legged robots use single or multi-microcontroller systems to control their motions. This work is a complete robot control system, implemented as a Multi-Processor System-on-Chip (MPSoC), on a Spartan 3A Field Programmable Gate Array (FPGA). Novel features of this system include encapsulation of the various levels of control, low communication latency between processors (4 clock cycles at 50 MHz), and ease of use for the control system researchers. The MPSoC implementation combines the performance benefits of processing control loops in parallel, with the size and mass advantages of a single IC solution. The system comprises one soft processor that is used for high-level decisions regarding the robot's overall movements, and six soft processors that run independent, low-level control loops for each of the six legs. The low-level control loop frequency can reach up to 2 kHz, and is only limited by the Analog to Digital Converter (ADC) sample rate. Coordination between legs occurs at 100 Hz. This design uses 90% of the user I/Os, 57% of the flip flops, 70% of the LUTs, 18% of the DSPs and 89% of the block RAMs on the FPGA, with a system operating frequency of 50 MHz. A legged robot, Abigaille-III, uses this control system to walk on flat and uneven surfaces.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".