Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot

This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors.  Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles

Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-based Control Paradigm

In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles, impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline

Fast Nonlinear Model Predictive Control of Quadrotors: Design and Experiments

Quadrotor (or quadcopter) is a type of Unmanned Aerial Vehicles (UAVs). Due to the quadrotors simple and inexpensive design, they have become popular platforms. This thesis proposes a computationally fast scheme for implementing Nonlinear Model Predictive Control (NMPC) as a high-level controller to solve the path following problem for unmanned quadrotors. After discussing the background and reviewing the literature, it is noted that this problem referred widely in the literature as a necessary step toward the autonomous flight of quadrotor UAVs. The previous studies usually used simplified models which are computationally uncomplicated and straightforward in terms of control developments and stability investigations. Moreover, some articles are presented showing the importance of accurate state observation on the performance of feedback-based control approaches. The NMPC-based controller is designed using a more realistic highly nonlinear control-oriented model which requires heavy computations for practical real-time implementations. To deal with this issue, the Newton generalized minimal residual (Newton/GMRES) method is applied to solve the NMPC’s real-time optimizations rapidly during the control process. This technique uses the Hamiltonian method to derive a set of equations with multiple variables. To solve these in a real-time application, the Newton/GMRES method applies forward-difference generalized minimal residual (fdgmres) algorithm. The simulation and experimental result using a commercial drone, called AR.Drone 2.0, in our laboratory instrumented by a Vicon Vantage motion capture system, demonstrate that our feedback-based control method’s performance highly depends on the reliability of the state vector feedback signals. As a result, a Kalman filter and Luenberger observer algorithms are used for estimating unknown states. The NMPC-based controller operation is simulated, and the result reveals the similar efficiency of observers. Moreover, the NMPC control approach is compared with a proportional controller which shows great improvements in the response of the quadrotor. The experiment showed that our control method is sufficiently fast for practical implementations, and it can solve the trajectory tracking problem properly even for complex paths. This thesis is concluded by stating a summary of contributions and some potential future works