Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach
By Federico Augugliaro, Angela P. Schoellig, Raffaello D'Andrea. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012
353 citations recorded by the source corpus.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/8ef8ab4903f1cfc8eef58ddbae7f7758f6330af2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
In Computer Vision and Pattern Recognition
- Jonathan P. How
- Michael Everett
- Yu Fan Chen
- Alexey S. Matveev
- T. K. Satish Kumar
- G. Sukhatme
- Michael Hoy
- Andrey V. Savkin
In Control and Systems Engineering
- Michael Everett
- Alexey S. Matveev
- T. K. Satish Kumar
- Ping Lu
- Yu Fan Chen
- Jonathan P. How
- G. Sukhatme
- Michael Hoy
Citations in the corpus
- Trajectory Planning for Quadrotor Swarms (2018, methodology): “double-integrators [7], our method produces trajectories with arbitrary smoothness up to a user-defined derivative.”
- Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, background): “62 • Other approaches have been proposed using multiplexed MPC (see, e.g., [136, 255]), and sequential decentralization (see, e.g., [3, 13, 194]).”
- Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search (2015, neutral): “quently used to control autonomous aerial vehicles such as quadrotors [38], [35], [34], [3], [2], [4], [29].”
- Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning (2016, background): “Yet, in many existing works, such constraints can be difficult to encode and might lead to a substantial increase in computational complexity [5], [12].”
- Survey of convex optimization for aerospace applications (2017, background): “Nevertheless, this problem can be efficiently solved by sequential convex programming (SCP) after the nonconvex constraints are approximated by convex ones [5].”
- Swarm of micro flying robots in the wild (2022, methodology): “We conducted more tests with an extra pair of centralized swarm trajectory planners, SCP (sequential convex programming) (48) and RBP (relative Bernstein polynomial) (49), both of which require global maps and sufficient time for offline computation, as given in the Supplementary Materials.”