TY - JOUR
T1 - Distributed Motion Planning for Multiple Quadrotors in Presence of Wind Gusts
AU - Abichandani, Pramod
AU - Lobo, Deepan
AU - Muralidharan, Meghna
AU - Runk, Nathan
AU - McIntyre, William
AU - Bucci, Donald
AU - Benson, Hande
N1 - Funding Information:
Funding for this work came from Lockheed Martin Advanced Technology Laboratories, Drexel University, and the New Jersey Institute of Technology through multiple internal research and development (IRAD) grants.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/1
Y1 - 2023/1
N2 - This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans.
AB - This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans.
KW - control barrier functions
KW - dryden wind model
KW - mathematical optimization
KW - multi-UAV systems
KW - quadrotor motion planning
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U2 - 10.3390/drones7010058
DO - 10.3390/drones7010058
M3 - Article
AN - SCOPUS:85146816423
SN - 2504-446X
VL - 7
JO - Drones
JF - Drones
IS - 1
M1 - 58
ER -