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

PID-based with Odometry for Trajectory Tracking Control on Four-wheel Omnidirectional Covid-19 Aromatherapy Robot

Inhalation therapy is one of the most popular treatments for many pulmonary conditions. The proposed Covid-19 aromatherapy robot is a type of Unmanned Ground Vehicle (UGV) mobile robot that delivers therapeutic vaporized essential oils or drugs needed to prevent or treat Covid-19 infections. It uses four omnidirectional wheels with a controlled speed to possibly move in all directions according to its trajectory. All motors for straight, left, or right directions need to be controlled, or the robot will be off-target. The paper presents omnidirectional four-wheeled robot trajectory tracking control based on PID and odometry. The odometry was used to obtain the robot's position and orientation, creating the global map. PID-based controls are used for three purposes: motor speed control, heading control, and position control. The omnidirectional robot had successfully controlled the movement of its four wheels at low speed on the trajectory tracking with a performance criterion value of 0.1 for the IAEH, 4.0 for MAEH, 0.01 for RMSEH, 0.00 for RMSEXY, and 0.06 for REBS. According to the experiment results, the robot's linear velocity error rate is 2%, with an average test value of 1.3 percent. The robot heading effective error value on all trajectories is 0.6%. The robot's direction can be monitored and be maintained at the planned trajectory. Doi: 10.28991/esj-2021-SPER-13 Full Text: PD

Guiding Vector Field Algorithm for a Moving Path Following Problem

This paper presents a guidance algorithm solving the problem of moving path following, that is, steering a mobile robot to a curvilinear path attached to a moving frame. The nonholonomic robot is described by the unicycle-type model under the influence of some constant exogenous disturbance. The desired path may be an arbitrary smooth curve in its implicit form, that is, a level set of some known smooth function. The path following algorithm employs a guiding vector field, whose integral curves converge to the trajectory. Experiments with a real fixed wing unmanned aerial vehicle (UAV) as well as numerical simulations are presented, illustrating the performance of the proposed path following control algorithm

Feature-based motion control for near-repetitive structures

In many manufacturing processes, production steps are carried out on repetitive structures which consist of identical features placed in a repetitive pattern. In the production of these repetitive structures one or more consecutive steps are carried out on the features to create the final product. Key to obtaining a high product quality is to position the tool with respect to each feature of the repetitive structure with a high accuracy. In current industrial practice, local position sensors such as motor encoders are used to separately measure the metric position of the tool and the stage where the repetitive structure is on. Here, the final accuracy of alignment directly relies on assumptions like thermal stability, infinite machine frame stiffness and constant pitch between successive features. As the size of these repetitive structures is growing, often these assumptions are difficult to satisfy in practice. The main goal of this thesis is to design control approaches for accurately positioning the tool with respect to the features, without the need of the aforementioned assumptions. In this thesis, visual servoing, i.e., using machine vision data in the servo loop to control the motion of a system, is used for controlling the relative position between the tool and the features. By using vision as a measurement device the relevant dynamics and disturbances are therefore measurable and can be accounted for in a non-collocated control setting. In many cases, the pitch between features is subject to small imperfections, e.g., due to the finite accuracy of preceding process steps or thermal expansion. Therefore, the distance between two features is unknown a priori, such that setpoints can not be constructed a priori. In this thesis, a novel feature-based position measurement is proposed, with the advantage that the feature-based target position of every feature is known a priori. Motion setpoints can be defined from feature to feature without knowing the exact absolute metric position of the features beforehand. Next to feature-to-feature movements, process steps involving movements with respect to the features, e.g., engraving or cutting, are implemented to increase the versatility of the movements. Final positioning accuracies of 10 µm are attained. For feature-to-feature movements with varying distances between the features a novel feedforward control strategy is developed based on iterative learning control (ILC) techniques. In this case, metric setpoints from feature to feature are constructed by scaling a nominal setpoint to handle the pitch imperfections. These scale varying setpoints will be applied during the learning process, while second order ILC is used to relax the classical ILC boundary of setpoints being the same every trial. The final position accuracy is within 5 µm, while scale varying setpoints are applied. The proposed control design approaches are validated in practice on an industrial application, where the task is to position a tool with respect to discrete semiconductors of a wafer. A visual servoing setup capable of attaining a 1 kHz frame rate is realized. It consists of an xy-stage on which a wafer is clamped which contains the discrete semiconductor products. A camera looks down onto the wafer and is used for position feedback. The time delay of the system is 2.5 ms and the variation of the position measurement is 0.3 µm (3s)

Robust Control of Nonlinear Systems with applications to Aerial Manipulation and Self Driving Cars

This work considers the problem of planning and control of robots in an environment with obstacles and external disturbances. The safety of robots is harder to achieve when planning in such uncertain environments. We describe a robust control scheme that combines three key components: system identification, uncertainty propagation, and trajectory optimization. Using this control scheme we tackle three problems. First, we develop a Nonlinear Model Predictive Controller (NMPC) for articulated rigid bodies and apply it to an aerial manipulation system to grasp object mid-air. Next, we tackle the problem of obstacle avoidance under unknown external disturbances. We propose two approaches, the first approach using adaptive NMPC with open- loop uncertainty propagation and the second approach using Tube NMPC. After that, we introduce dynamic models which use Artificial Neural Networks (ANN) and combine them with NMPC to control a ground vehicle and an aerial manipulation system. Finally, we introduce a software framework for integrating the above algorithms to perform complex tasks. The software framework provides users with the ability to design systems that are robust to control and hardware failures where preventive action is taken before-hand. The framework also allows for safe testing of control and task logic in simulation before evaluating on the real robot. The software framework is applied to an aerial manipulation system to perform a package sorting task, and extensive experiments demonstrate the ability of the system to recover from failures. In addition to robust control, we present two related control problems. The first problem pertains to designing an obstacle avoidance controller for an underactuated system that is Lyapunov stable. We extend a standard gyroscopic obstacle avoidance controller to be applicable to an underactuated system. The second problem addresses the navigation of an Unmanned Ground Vehicle (UGV) on an unstructured terrain. We propose using NMPC combined with a high fidelity physics engine to generate a reference trajectory that is dynamically feasible and accounts for unsafe areas in the terrain

Adaptive Sliding Mode Tracking Control of Mobile Robot in Dynamic Environment Using Artificial Potential Fields

Solution to the safe and collision-free trajectory of the wheeled mobile robot in cluttered environments containing the static and/or dynamic obstacle has become a very popular and challenging research topic in the last decade. Notwithstanding of the amount of publications dealing with the different aspects of this field, the ongoing efforts to address the more effective and creative methods is continued. In this article, the effectiveness of the real-time harmonic potential field theory based on the panel method to generate the reference path and the orientation of the trajectory tracking control of the three-wheel nonholonomic robot in the presence of variable-size dynamic obstacle is investigated. The hybrid control strategy based on a backstepping kinematic and regressor-based adaptive integral sliding mode dynamic control in the presence of disturbance in the torque level and parameter uncertainties is employed. In order to illustrate the performance of the proposed adaptive algorithm, a hybrid conventional integral sliding mode dynamic control has been established. The employed control methods ensure the stability of the controlled system according to Lyapunov’s stability law. The results of simulation program show the remarkable performance of the both methods as the robust dynamic control of the mobile robot in tracking the reference path in unstructured environment containing variable-size dynamic obstacle with outstanding disturbance suppression characteristic