Energy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy
Overview
This research presents an energy-aware open-vocabulary detection (OVD) scheduler designed for low-power mobile robots.
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Event evidence and corrections
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Reported quantity · trials: 40 other · reported trials
“low-, mid-, and high-power YOLO-World-S modes on a Jetson Orin Nano. We evaluate the energy-quality trade-off on an AgileX LIMO using 40 real navigation trials: two target objects, five placements per target-policy pair, and four perception policies. Across bottle and cup trials, the scheduler records 8/10 mission success compared with 7/10 for each fixed-rate baseline, while reducing mean Jetson input power (a Jetso”
Exact source · revision 1Source owner not reported
Reported quantity · mean_power_baseline: 8.045 other · Basis not reported
“n-side power-draw metric) by 5.2% relative to an 8 Hz high-rate baseline and lowering achieved OVD call rate by 34.0%. It does not minimize mission-window Jetson input energy because longer missions can erase instantaneous power savings. A separate 60 s stationary microbenchmark isolates the perception workload: the scheduler lowers mean power from 8.045 W to 7.301 W and energy from 484.7 J to 438.1 J relative to the”
Exact source · revision 1Source owner not reported
Reported quantity · mean_power_scheduler: 7.301 other · Basis not reported
“n-side power-draw metric) by 5.2% relative to an 8 Hz high-rate baseline and lowering achieved OVD call rate by 34.0%. It does not minimize mission-window Jetson input energy because longer missions can erase instantaneous power savings. A separate 60 s stationary microbenchmark isolates the perception workload: the scheduler lowers mean power from 8.045 W to 7.301 W and energy from 484.7 J to 438.1 J relative to the”
Exact source · revision 1Source owner not reported
Reported quantity · energy_baseline: 484.7 other · Basis not reported
“n-side power-draw metric) by 5.2% relative to an 8 Hz high-rate baseline and lowering achieved OVD call rate by 34.0%. It does not minimize mission-window Jetson input energy because longer missions can erase instantaneous power savings. A separate 60 s stationary microbenchmark isolates the perception workload: the scheduler lowers mean power from 8.045 W to 7.301 W and energy from 484.7 J to 438.1 J relative to the”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalEnergy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy
This research presents an energy-aware open-vocabulary detection (OVD) scheduler designed for low-power mobile robots.
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