Trajectory Generation for Drones in Confined Spaces Using an Ellipsoid Model of the Body

Sabetghadam, Bahareh; Cunha, Rita; Pascoal, António

doi:10.1109/lcsys.2021.3088406

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(See Figure 2). As proposed in [28], collision avoidance constraints for two ellipsoid-shaped drones can be derived using separating hyperplanes. Denoting by a ∈ R 3 and b ∈ R the normal vector and offset of a hyperplane, respectively, the separating hyperplane for E i and E j , defined as H ≡ {p|a T p − b = 0}, must satisfy…”

Section: Collision Avoidancementioning

confidence: 99%

A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning

Sabetghadam

Cunha

Pascoal

2022

Sensors

Self Cite

View full text Add to dashboard Cite

In this paper, we present a distributed algorithm to generate collision-free trajectories for a group of quadrotors flying through a common workspace. In the setup adopted, each vehicle replans its trajectory, in a receding horizon manner, by solving a small-scale optimization problem that only involves its own individual variables. We adopt the Voronoi partitioning of space to derive local constraints that guarantee collision avoidance with all neighbors for a certain time horizon. The obtained set of collision avoidance constraints explicitly takes into account the vehicle’s orientation to avoid infeasiblity issues caused by ignoring the quadrotor’s rotational motion. Moreover, the resulting constraints can be expressed as Bézier curves, and thus can be evaluated efficiently, without discretization, to ensure that collision avoidance requirements are satisfied at any time instant, even for an extended planning horizon. The proposed approach is validated through extensive simulations with up to 100 drones. The results show that the proposed method has a higher success rate at finding collision-free trajectories for large groups of drones compared to other Voronoi diagram-based methods.

show abstract

Section: Collision Avoidancementioning

confidence: 99%