RADAR Perception for Dynamic Obstacle Avoidance onboard small-scale Quadrotor UAVs
RADAR Perception for Dynamic Obstacle Avoidance onboard small-scale Quadrotor UAVs
This paper introduces the first mmWave RADAR-based perception-and-control system designed for fast dynamic obstacle avoidance (DOA) on uncrewed aerial vehicles.
The study presents a mmWave RADAR-based perception and control system for dynamic obstacle avoidance on small-scale quadrotors, validated through tethered indoor flights and untethered outdoor maneuvers under varying visibility and smoke conditions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
position errors
≤0.15
View original evidence
BF) controller that directly outputs evasive accelerations. In 300 throw experiments spanning three object sizes and varying visibility (light and dark), it achieves mean position errors of at most 0.15 m, 0.93m, and 0.87m in the x, y, and z directions, with a similar spread in 90 experiments in smoke. An onboard implementation on a Raspberry Pi 4B demonstrates real-time feasibility with an end-to-end processing late
Open source S4system latency
≈14
View original evidence
ncy of approximately 14ms per RADAR frame and a system latency of 75ms from first detection to the avoidance command. The UAV successfully avoided obstacles in all 20 tethered indoor flights and all 39 untethered outdoor avoidance maneuvers across three ball sizes, during both hover and forward flights at up to 5m/s with measured closing speeds up to 14.6m/s.
Open source S5system latency
75
View original evidence
ncy of approximately 14ms per RADAR frame and a system latency of 75ms from first detection to the avoidance command. The UAV successfully avoided obstacles in all 20 tethered indoor flights and all 39 untethered outdoor avoidance maneuvers across three ball sizes, during both hover and forward flights at up to 5m/s with measured closing speeds up to 14.6m/s.
Open source S5closing speeds
≤14.6
View original evidence
ncy of approximately 14ms per RADAR frame and a system latency of 75ms from first detection to the avoidance command. The UAV successfully avoided obstacles in all 20 tethered indoor flights and all 39 untethered outdoor avoidance maneuvers across three ball sizes, during both hover and forward flights at up to 5m/s with measured closing speeds up to 14.6m/s.
Open source S5forward flight speeds
≤5
View original evidence
ncy of approximately 14ms per RADAR frame and a system latency of 75ms from first detection to the avoidance command. The UAV successfully avoided obstacles in all 20 tethered indoor flights and all 39 untethered outdoor avoidance maneuvers across three ball sizes, during both hover and forward flights at up to 5m/s with measured closing speeds up to 14.6m/s.
Open source S5throw experiments
300
Reported trials
View original evidence
BF) controller that directly outputs evasive accelerations. In 300 throw experiments spanning three object sizes and varying visibility (light and dark), it achieves mean position errors of at most 0.15 m, 0.93m, and 0.87m in the x, y, and z directions, with a similar spread in 90 experiments in smoke. An onboard implementation on a Raspberry Pi 4B demonstrates real-time feasibility with an end-to-end processing late
Open source S4
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ncy of approximately 14ms per RADAR frame and a system latency of 75ms from first detection to the avoidance command. The UAV successfully avoided obstacles in all 20 tethered indoor flights and all 39 untethered outdoor avoidance maneuvers across three ball sizes, during both hover and forward flights at up to 5m/s with measured closing speeds up to 14.6m/s.
Open source S5
BF) controller that directly outputs evasive accelerations. In 300 throw experiments spanning three object sizes and varying visibility (light and dark), it achieves mean position errors of at most 0.15 m, 0.93m, and 0.87m in the x, y, and z directions, with a similar spread in 90 experiments in smoke. An onboard implementation on a Raspberry Pi 4B demonstrates real-time feasibility with an end-to-end processing late
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org