Real-Time Distributed Motion Planning with Submodular Minimization
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
RISC Laboratory
Date
2018-11-16Online Publication Date
2018-11-16Print Publication Date
2018-08Permanent link to this record
http://hdl.handle.net/10754/630606
Metadata
Show full item recordAbstract
We present a real-time implementation of a distributed motion planning framework that is based on model predictive control with one step prediction horizon and submodular function minimization. In particular, our focus is to evaluate the real-time performance of this distributed motion coordination framework. For performance evaluation, we develop a realistic simulation environment for the challenging setup of capture the flag game, which is played between two teams. We consider a scenario in which each team has four quadcopters and the game is played in an arena with multiple obstacles. We develop the simulation setup primarily in Gazebo with software in the loop. The software in the loop is the autopilot software, which is used to stabilize and control the motion of each quadcopter. The motion plan for the defense team is computed by minimizing submodular potential functions using the distributed and online algorithm presented in our previous work. Based on extensive simulations under various conditions, we verify that the proposed approach can be used effectively for real-time distributed control of multiagent systems over discrete input space.Citation
Jaleel H, Abdelkader M, Shamma JS (2018) Real-Time Distributed Motion Planning with Submodular Minimization. 2018 IEEE Conference on Control Technology and Applications (CCTA). Available: http://dx.doi.org/10.1109/ccta.2018.8511426.Conference/Event name
2nd IEEE Conference on Control Technology and Applications, CCTA 2018Additional Links
https://ieeexplore.ieee.org/document/8511426ae974a485f413a2113503eed53cd6c53
10.1109/ccta.2018.8511426