37 research outputs found
Virtual Omnidirectional Perception for Downwash Prediction within a Team of Nano Multirotors Flying in Close Proximity
Teams of flying robots can be used for inspection, delivery, and construction
tasks, in which they might be required to fly very close to each other. In such
close-proximity cases, nonlinear aerodynamic effects can cause catastrophic
crashes, necessitating each robots' awareness of the surrounding. Existing
approaches rely on multiple, expensive or heavy perception sensors. Such
perception methods are impractical to use on nano multirotors that are
constrained with respect to weight, computation, and price. Instead, we propose
to use the often ignored yaw degree-of-freedom of multirotors to spin a single,
cheap and lightweight monocular camera at a high angular rate for
omnidirectional awareness of the neighboring robots. We provide a dataset
collected with real-world physical flights as well as with 3D-rendered scenes
and compare two existing learning-based methods in different settings with
respect to success rate, relative position estimation, and downwash prediction
accuracy. We demonstrate that our proposed spinning camera is capable of
predicting the presence of aerodynamic downwash with an score of over 80%
in a challenging swapping task
Downwash-Aware Trajectory Planning for Large Quadrotor Teams
We describe a method for formation-change trajectory planning for large
quadrotor teams in obstacle-rich environments. Our method decomposes the
planning problem into two stages: a discrete planner operating on a graph
representation of the workspace, and a continuous refinement that converts the
non-smooth graph plan into a set of C^k-continuous trajectories, locally
optimizing an integral-squared-derivative cost. We account for the downwash
effect, allowing safe flight in dense formations. We demonstrate the
computational efficiency in simulation with up to 200 robots and the physical
plausibility with an experiment with 32 nano-quadrotors. Our approach can
compute safe and smooth trajectories for hundreds of quadrotors in dense
environments with obstacles in a few minutes.Comment: 8 page
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-proximity flight of multirotor swarms. Close-proximity control is challenging due to the complex aerodynamic interaction effects between multirotors, such as downwash from higher vehicles to lower ones. Conventional methods often fail to properly capture these interaction effects, resulting in controllers that must maintain large safety distances between vehicles, and thus are not capable of close-proximity flight. Our approach combines a nominal dynamics model with a regularized permutation-invariant Deep Neural Network (DNN) that accurately learns the high-order multi-vehicle interactions. We design a stable nonlinear tracking controller using the learned model. Experimental results demonstrate that the proposed controller significantly outperforms a baseline nonlinear tracking controller with up to four times smaller worst-case height tracking errors. We also empirically demonstrate the ability of our learned model to generalize to larger swarm sizes
GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
We present GLAS: Global-to- Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time
db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning
This paper presents a multi-robot kinodynamic motion planner that enables a
team of robots with different dynamics, actuation limits, and shapes to reach
their goals in challenging environments. We solve this problem by combining
Conflict-Based Search (CBS), a multi-agent path finding method, and
discontinuity-bounded A*, a single-robot kinodynamic motion planner. Our
method, db-CBS, operates in three levels. Initially, we compute trajectories
for individual robots using a graph search that allows bounded discontinuities
between precomputed motion primitives. The second level identifies inter-robot
collisions and resolves them by imposing constraints on the first level. The
third and final level uses the resulting solution with discontinuities as an
initial guess for a joint space trajectory optimization. The procedure is
repeated with a reduced discontinuity bound. Our approach is anytime,
probabilistically complete, asymptotically optimal, and finds near-optimal
solutions quickly. Experimental results with robot dynamics such as unicycle,
double integrator, and car with trailer in different settings show that our
method is capable of solving challenging tasks with a higher success rate and
lower cost than the existing state-of-the-art.Comment: submitted to ICRA 202