Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams
By Daniel Mellinger, Aleksandr Kushleyev, Vijay R. Kumar. IEEE International Conference on Robotics and Automation, 2012
303 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/373471553fadd638de24fe73bb286cfe06a940e2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Charles Richter
- Jonathan P. How
- Nicholas A. Roy
- Michael Everett
- Yu Fan Chen
- G. Sukhatme
- Adam Bry
- Vijay R. Kumar
In Computational Theory and Mathematics
- Nicholas A. Roy
- Charles Richter
- Adam Bry
- Vijay R. Kumar
- Nathan Michael
- Jonathan P. How
- G. Sukhatme
- Daniel Mellinger
In Computer Vision and Pattern Recognition
- Vijay R. Kumar
- Markus Achtelik
- Nathan Michael
- Daniel Mellinger
- Nicholas A. Roy
- Jonathan P. How
- Michael Watterson
- Aleksandr Kushleyev
Citations in the corpus
- Trajectory Planning for Quadrotor Swarms (2018, background): “Some authors have solved the formation change problem in a continuous setting [7], [8], but such methods are often tightly coupled, solving one large optimization problem in which the”
- Towards a swarm of agile micro quadrotors (2012, background): “The computational complexity of the optimization problem is discussed further in Mellinger et al. (2012).”
- Safety Barrier Certificates for Collisions-Free Multirobot Systems (2017, unknown): “A mixed-integer quadratic program approach was proposed in [12] to plan robot trajectories that avoid both inter-robot collisions and static obstacles.”
- Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments (2016, methodology): “Bsplines [23] and Legendre polynomials [20] have been used to avoid ill-conditioning in trajectory optimization problems, however these options preclude the efficient method presented here.”
- Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “…0.5 m diameter, and 5000 rpm nominal rotor speed at hover) consumes about 75 W. Attempts to develop a smaller quadrotor (Miller et al., 2010; Mellinger et al., 2012) suggest that a quadrotor of mass around 50–60 g, 0.075 m diameter, and between 8000 and 9000 rpm rotor speed consumes a…”
- Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning (2016, methodology): “However, centralized path planning methods can be computationally prohibitive for large teams [1].”
- Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments (2017, methodology): “Generating a collisionfree trajectory has been solved with Mixed Integer methods in [3]–[5].”