143 research outputs found
Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation
Autonomous navigation in unknown environments with obstacles remains
challenging for micro aerial vehicles (MAVs) due to their limited onboard
computing and sensing resources. Although various collision avoidance methods
have been developed, it is still possible for drones to collide with unobserved
obstacles due to unpredictable disturbances, sensor limitations, and control
uncertainty. Instead of completely avoiding collisions, this article proposes
Air Bumper, a collision detection and reaction framework, for fully autonomous
flight in 3D environments to improve the safety of drones. Our framework only
utilizes the onboard inertial measurement unit (IMU) to detect and estimate
collisions. We further design a collision recovery control for rapid recovery
and collision-aware mapping to integrate collision information into general
LiDAR-based sensing and planning frameworks. Our simulation and experimental
results show that the quadrotor can rapidly detect, estimate, and recover from
collisions with obstacles in 3D space and continue the flight smoothly with the
help of the collision-aware map. Our Air Bumper will be released as open-source
software on GitHub
AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space
Quadrotor motion planning in complex environments leverage the concept of
safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically,
SFCs are constructed through convex decomposition of the environment's free
space into cuboids, convex polyhedra, or spheres. However, when dealing with a
quadrotor swarm, such SFCs can be overly conservative, substantially limiting
the available free space for quadrotors to coordinate. This paper presents an
Alternating Minimization-based approach that does not require building a
conservative free-space approximation. Instead, both static and dynamic
collision constraints are treated in a unified manner. Dynamic collisions are
handled based on shared position trajectories of the quadrotors. Static
obstacle avoidance is coupled with distance queries from the Octomap, providing
an implicit non-convex decomposition of free space. As a result, our approach
is scalable to arbitrary complex environments. Through extensive comparisons in
simulation, we demonstrate a improvement in success rate, an average
reduction in mission completion time, and an average
reduction in per-agent computation time compared to SFC-based approaches. We
also experimentally validated our approach using a Crazyflie quadrotor swarm of
up to 12 quadrotors in obstacle-rich environments. The code, supplementary
materials, and videos are released for reference.Comment: Submitted to ICRA 202
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