Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

This letter introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control. Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbors, the agents are able to detect and avoid collisions while moving toward their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming, while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.

[1]  Carlos Carbone,et al.  Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture , 2018 .

[2]  Dinesh Manocha,et al.  Reciprocal collision avoidance with acceleration-velocity obstacles , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Francois Defay,et al.  Collision-Free Rendezvous Maneuvers for Formations of Unmanned Aerial Vehicles , 2017 .

[4]  Goele Pipeleers,et al.  Distributed model predictive formation control with inter-vehicle collision avoidance , 2017, 2017 11th Asian Control Conference (ASCC).

[5]  Yuanqing Xia,et al.  Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance , 2017, J. Frankl. Inst..

[6]  Gaurav S. Sukhatme,et al.  Downwash-aware trajectory planning for large quadrotor teams , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Vijay Kumar,et al.  Decentralized formation control with variable shapes for aerial robots , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Baocang Ding,et al.  A synthesis approach of distributed model predictive control for homogeneous multi-agent system with collision avoidance , 2014, Int. J. Control.

[9]  Paul A. Beardsley,et al.  Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots , 2010, DARS.

[10]  Jonathan P. How,et al.  Decoupled multiagent path planning via incremental sequential convex programming , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[12]  Eric Guizzo,et al.  Three Engineers, Hundreds of Robots, One Warehouse , 2008, IEEE Spectrum.

[13]  Vijay Kumar,et al.  A Complete Algorithm for Generating Safe Trajectories for Multi-robot Teams , 2015, ISRR.

[14]  Gary Hewer,et al.  An Efficient Algorithm for Optimal Trajectory Generation for Heterogeneous Multi-Agent Systems in Non-Convex Environments , 2018, IEEE Robotics and Automation Letters.

[15]  Angela P. Schoellig,et al.  Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Vijay Kumar,et al.  Distributed Optimization with Pairwise Constraints and its Application to Multi-robot Path Planning , 2010, Robotics: Science and Systems.

[17]  Vijay Kumar,et al.  Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Pengkai Ru,et al.  Nonlinear Model Predictive Control for Cooperative Control and Estimation , 2017 .

[19]  B. Moor,et al.  Mixed integer programming for multi-vehicle path planning , 2001, 2001 European Control Conference (ECC).

[21]  Farzaneh Abdollahi,et al.  A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots , 2014, IEEE Transactions on Industrial Electronics.