Trajectory tracking for quadrotors: An optimization‐based planning followed by controlling approach

We present an optimization‐based reference trajectory tracking method for quadrotor robots for slow‐speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy with linear quadratic Gaussian in which nonlinear model...

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Bibliographic Details
Published in:Journal of field robotics 2022-10, Vol.39 (7), p.1003-1013
Main Authors: Kulathunga, Geesara, Devitt, Dmitry, Klimchik, Alexandr
Format: Article
Language:English
Subjects:
Algorithms
Constraint modelling
Euclidean distance transformation map
Euclidean geometry
Iterative methods
linear quadratic Gaussian
motion planning
multiple shooting
Nonlinear control
nonlinear model predictive control
obstacle avoidance
Optimal control
Optimization
Predictive control
Rotary wing aircraft
Tracking
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Summary:We present an optimization‐based reference trajectory tracking method for quadrotor robots for slow‐speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy with linear quadratic Gaussian in which nonlinear model predictive control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple‐shooting is suggested over direct‐collocation for imposing constraints when modeling the NMPC. Incremental Euclidean distance transformation map is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor's current pose and the desired reference trajectory pose in each iteration. Finally, we compared the proposed method with two other approaches and showed that the proposed method outperforms the said approaches in terms of reaching the goal without any collision. Additionally, we published a new data set that can be used for evaluating the performance of trajectory tracking algorithms.
ISSN:1556-4959
1556-4967
DOI:10.1002/rob.22084