MPC-based path following control of an omnidirectional mobile robot with consideration of robot constraints

In this paper, the path following problem of an omnidirectional mobile robot (OMR) has been studied. Unlike nonholonomic mobile robots, translational and rotational movements of OMRs can be controlled simultaneously and independently. However the constraints of translational and rotational velocities are coupled through the OMR's orientation angle. Therefore, a combination of a virtual-vehicle concept and a model predictive control (MPC) strategy is proposed in this work to handle both robot constraints and the path following problem. Our proposed control scheme allows the OMR to follow the reference path successfully and safely, as illustrated in simulation experiments. The forward velocity is close to the desired one and the desired orientation angle is achieved at a given point on the path, while the robot's wheel velocities are maintained within boundaries

Koordinirano slijeđenje putanje za mobilne robote zasnovano na strategiji virtualne strukture i modelsko prediktivnom upravljanju

In this paper, we propose a novel coordinated path following controller based on  model predictive control (MPC) for mobile robots. The strategy is based on a virtual structure approach where the entire formation is considered as a rigid body and the control laws for a virtual leader vehicle and for actual follower robots are optimized by considering the dynamics of the virtual structure and the desired motion of each vehicle. Besides, we also fulfill time convergence for trajectory tracking by integrating an additional penalty term into our model predictive control scheme. However, the major concern in the use of model predictive control is whether such an open-loop control scheme can guarantee system stability. In this case, we apply the idea of a contractive constraint to guarantee the stability of our MPC framework. Although our approach is centralized, numerous simulation scenarios have been conducted to illustrate its effectiveness and its superior performance for a small group of mobile robots. Furthermore, we show that path following control can offer a number of advantages over its trajectory tracking counterpart.U ovome članku predlaže se novi algoritam upravljanja koordiniranim slijeđenjem putanje za mobilne robote zasnovan na modelsko prediktivnom upravljanju. Strategija se zasniva na pristupu s virtualnom strukturom gdje se cijela formacija robota smatra krutim tijelom, dok se za virtualno vodeće vozilo i za slijedeće robote optimiziraju zakoni upravljanja vodeći računa o dinamici virtualne strukture i željenom gibanju svakog od vozila. Također, algoritam ispunjava uvjet vremena konvergencije radi praćenja trajektorija na način da integrira dodatani član unutar algoritma modelsko prediktivnog upravljanja. Međutim, koristeći modelsko prediktivno upravljanje postavlja se pitanje može li ovakav pristup u otvorenom upravljačkom krugu jamčiti stabilnost. Radi toga, primijenjuje se ideja sužavajućeg ograničenja radi jamčenja stabilnosti predloženog riješenja. Iako je predloženi pristup centraliziran, provedeni su brojni simulacijski eksperimenti kako bi se ilustrirala učinkovitost i superiorno vladanje na primjeru male grupe mobilnih robota. Nadalje, pokazuje se da upravljanje slijeđenjem putanje pruža brojne prednosti u usporedbi s praćenjem trajektorije

Fast Color Quantization Using Weighted Sort-Means Clustering

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table

A snake-based scheme for path planning and control with constraints by distributed visual sensors

YesThis paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot’s non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle.Royal Society, Natural Science Funding Council (China

Improving the Performance of K-Means for Color Quantization

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.Comment: 26 pages, 4 figures, 13 table

Development of an automatic chili drying controller based on computer vision

Color of dried chilies is one of the most key factors that influences consumers’ preference. It is mainly affected by the drying process. Traditionally, drying temperature is fixed during the drying process without regard to the quality of chilies being dried. Thus, the aim of this research is to develop a controller for the chili drying process that determines the drying temperature according to the change of chili color. To measure the chili color, we built a hot air tray dryer incorporated with a computer vision system which measures the chili color during drying. We converted the RGB color model to the L*a*b* color model and calculated the change of the a* component (\delta a). If \delta a within 15 minutes was less than 2, then, the drying temperature was set to 90°C for ten minutes and set back to 80°C, otherwise, it was set to 80°C. The experimental results showed that the drying time was shortened, compared to the 80°C drying temperature. The color of dried chilies was improved, compared to the 90°C drying temperature

Koordinierte Pfadverfolgungsregelung und Formationskontrolle mobiler Roboter

Rapid advances in sensing, computing and communication technologies have led to considerably increased research activities in multi-robot systems over the last decade. Topics include multi-robot motion planning, cooperative manipulation, aerial applications involving cooperative exploration of the unknown environment, automated highway systems, software architectures for multi-robot systems, and formation control. Multi-robot systems have been proven to offer additional advantages in terms of flexibility in operating a group of robots and failure tolerance due to redundancy in available mobile robots. However, the benefits of using multi-robot teams do not come without cost. Coordinating teams of autonomous robots is much more challenging than maneuvering a single robot. This dissertation addresses formation control problems, which are among the most active research topics in multi-robot systems. Over the last two decades, there have been a large number of publications on this field, and it is still growing. Recently, this research has been extended to some related research areas, e.g., consensus problems and distributed control systems, imposing new challenges on formation control problems. In general, formation control subproblems addressed in the literature can be classified as formation shape generation, formation reconfiguration/selection, formation tracking, and role assignment in formation. The main purpose of this dissertation is to address two important and correlated subproblems in formation control: formation tracking and role assignment in formation. The goal of the former is that a team of mobile robots is required to maintain a geometric formation while tracking a reference or a set of references. The latter arises when a mobile robot in the team must decide what role to take on in a desired formation configuration. In particular, we study coordinated path following control of omnidirectional mobile robots and unicycle mobile robots. This problem can be seen as a subtask of formation tracking. Path following is one of the three basic motion control tasks in mobile robot research. The others are trajectory tracking and point stabilization. Even though less attention is drawn to this problem in the literature, it offers some advantages over trajectory tracking in some cases. The objective of path following control is to be on the path rather than at a certain point at a particular time. To solve this problem, we employ a model predictive control (MPC) technique to generate a sequence of optimal velocities of a so-called virtual vehicle which is followed by a real robot. This approach can eliminate stringent initial condition constraints because the velocity of a virtual vehicle is controlled explicitly. Using this technique, we can gain some benefits over other available control schemes, e.g., the ability to incorporate generic models, linear and nonlinear, and constraints in the optimal control problem and the ability to use future values of references when they are available, allowing to improve system performance. However, the main drawback is significant computational burden associated with solving a set of nonlinear differential equations and a nonlinear dynamic optimization problem online. Then, we extend path following control to coordinated path following control. A group of mobile robots not only follow a reference path but also maintain a geometric formation shape. The main challenge is to design a decentralized control law using only local information to achieve a formation tracking objective. In this study, we propose two solutions. In the first solution, the MPC framework for path following control is extended to the coordinated path following control problem. In spite of great theoretical properties of such MPC controllers, the stability and feasibility of decentralized schemes are rather conservative. The second solution is computationally simple so that it may be suitable for low-computational systems when the advantages of MPC schemes including constraint handling are not a dominating factor. Its controller design is based on a Lyapunov technique and a second-order consensus protocol with a reference velocity. It is worth noting that the path variable has been used as a coupling variable synchronizing each member in formation in both solutions. In the second formation control subproblem, we study role assignment in formation. This problem becomes more challenging when robots in the team do not have complete information and they do not know the number of robots participating in the formation tasks. With the assumption that the formation graph is connected and bidirectional, we propose an online and distributed role assignment. This approach is proven by extensive simulation and experimental results.Rapide Fortschritte in Sensor-, Rechen- und Kommunikationstechnologien haben in den letzten zehn Jahren zu verstärkter Forschung an Multi-Roboter-Systemen geführt. Multi-Roboter-Systeme haben sich als vorteilhaft hinsichtlich Flexibilität beim Betrieb einer Gruppe von Robotern sowie hinsichtlich Ausfallsicherheit durch Redundanz der verfügbaren mobilen Roboter erwiesen. Doch die Vorteile der Verwendung von Multi-Roboter-Teams verlangen nach der Lösung neuer Problemstellungen. Die Koordination von Teams autonomer Roboter ist erheblich schwieriger als die Steuerung eines einzelnen Roboters. Die vorliegende Dissertation behandelt Fragestellungen der Formationskontrolle, welche zu den aktivsten Forschungsthemen im Bereich Multi-Roboter-Systeme gehören. In den letzten zwei Jahrzehnten hat es eine große und weiter wachsende Zahl von Veröffentlichungen auf diesem Gebiet gegeben. Diese Dissertation behandelt im Kern zwei wichtige, miteinander interagierende Unterprobleme der Formationskontrolle: Formationsverfolgung und Rollenzuteilung innerhalb der Formation. Ziel der Formationsverfolgung ist es, dass eine Gruppe mobiler Roboter eine geometrische Formation einhält, während eine oder mehrere Referenzen verfolgt werden. Das Problem der Rollenzuteilung hingegen entsteht, wenn ein mobiler Roboter des Teams entscheiden muss, welche Rolle er in der gewünschten Formationskonfiguration übernimmt. Insbesondere behandelt diese Arbeit die koordinierte Pfadverfolgungsregelung mit omnidirektionalen mobilen Robotern und mit mobilen Robotern mit Differentialantrieb. Dieses Problem kann als eine Teilaufgabe der Formationskontrolle gesehen werden. Pfadverfolgung ist in der Wissenschaft eine der drei grundlegenden Aufgaben der Bewegungsregelung mobiler Roboter. Bei den anderen zweien handelt es sich um Trajektorienverfolgung und Punktstabilisierung. Obwohl dem Pfadverfolgungsproblem in der Literatur weniger Aufmerksamkeit zuteil wird, bietet es gegenüber Trajektorienverfolgung in einigen Fällen Vorteile. Das Ziel der Pfadverfolgungsregelung ist, sich auf einem Pfad zu befinden, anstatt an einem bestimmten Punkt zu einer bestimmten Zeit. Um dieses Problem zu lösen, wird eine Technik der modellbasierten prädiktiven Regelung (Model Predictive Control, MPC) verwendet: Eine Folge optimaler Geschwindigkeiten eines so genannten virtuellen Fahrzeuges wird berechnet, welche von einem realen Roboter befolgt wird. Dieser Ansatz kann strikte Nebenbedingungen der Anfangsbedingungen auflösen, da die Geschwindigkeit eines virtuellen Fahrzeuges explizit geregelt wird. Mit dieser Technik werden einige Vorteile gegenüber anderen verfügbaren Regelungssystemen erzielt, z.B. die Fähigkeit, generische Modelle, lineare und nichtlineare Nebenbedingungen sowie Nebenbedingungen des optimalen Regelungsproblems zu integrieren. Ferner ist es möglich, zukünftige Werte von Referenzen zu verwenden, wenn sie verfügbar sind, so dass die Systemleistung verbessert werden kann. Allerdings ist der größte Nachteil der erhebliche Rechenaufwand bei der Online-Lösung einer Reihe nichtlinearer Differentialgleichungen und eines nichtlinearen dynamischen Optimierungsproblems. Darüber hinaus wird in dieser Arbeit Pfadverfolgungsregelung auf koordinierte Pfadverfolgungsregelung erweitert. Eine Gruppe mobiler Roboter soll nicht nur einem Referenzpfad folgen, sondern dabei auch eine geometrische Formation einhalten. Die größte Herausforderung ist der Entwurf einer dezentralisierten Regelung unter alleiniger Verwendung lokaler Informationen zur Erzielung der Formationsverfolgung. In dieser Studie schlagen wir zwei Lösungen vor: Bei der ersten Lösung wird das MPC-Rahmenwerk für Pfadverfolgungsregelung auf das Problem der koordinierten Pfadverfolgungsregelung ausgeweitet. Trotz der hervorragenden theoretischen Eigenschaften solcher MPC-Regler sind Stabilität und Machbarkeit dezentralisierter Schemata eher verhalten. Der zweite Lösungsansatz ist rechentechnisch einfach, so dass er möglicherweise für Systeme mit eingeschränkter Rechenleistung einsetzbar ist, wann immer die Vorteile von MPC-Schemata mit ihrer Berücksichtigung von Nebenbedingungen nicht dominierend sind. Die Steuerung basiert auf einer Lyapunov-Technik und einem Konsensprotokoll zweiter Ordnung für eine vorgegebene Geschwindigkeit. Es ist erwähnenswert, dass der Pfad in beiden Lösungsansätzen als Variable zur Kopplung dient und jedes Mitglied der Formation synchronisiert. In der zweiten Unterfragestellung der Formationskontrolle wird Rollenzuweisung in Formationen untersucht. Dieses Problem wird schwieriger, wenn Roboter der Gruppe nicht über alle Informationen und nicht über die Zahl der teilnehmenden Roboter in der Formation verfügen. Unter der Annahme, dass der Formationsgraph zusammenhängend und ungerichtet ist, wird eine verteilte Online-Rollenzuteilung entwickelt. Dieser Ansatz wird durch ausführliche Simulationen und Experimente mit realen Robotern gestützt

A RECEDING HORIZON CONTROLLER FOR PATH FOLLOWING OF A MOBILE ROBOT WITH ACTUATOR CONSTRAINTS

In path following control, the forward velocity can be seen as an additional degree of freedom. However, many path following control laws assume that the forward velocity is a constant and actuators are ideal, i.e., no actuator constraints. Unfortunately, these controllers cannot be realized in real-world implementation. Thus, we propose an extension to an existing path following controller in order that the forward velocity can be varied according to the robot’s posture errors and the desired forward velocity. This can be done by adopting a receding horizon scheme, where the optimization problem is solved online along some look-ahead time intervals. Furthermore, we treat boundaries of wheel speed and acceleration as constraints in the optimization problem. As a result, a mobile robot can slow down when making a sharp turn and it can speed up when being behind a reference point. Simulation tests have been conducted in order to illustrate the effectiveness of our proposed scheme

LQR and MPC controller design and comparison for a stationary self-balancing bicycle robot with a reaction wheel

summary:A self-balancing bicycle robot based on the concept of an inverted pendulum is an unstable and nonlinear system. To stabilize the system in this work, the following three main components are required, i. e., (1) an IMU sensor that detects the tilt angle of the bicycle robot, (2) a controller that is used to control motion of a reaction wheel, and (3) a reaction wheel that is employed to produce reactionary torque to balance the bicycle robot. In this paper, we propose three control strategies: linear quadratic regulator (LQR), linear model predictive control (LMPC), and nonlinear model predictive control (NMPC). Several simulation tests have been conducted in order to show that our proposed control laws can achieve stabilizaton and make the system balance. Furthermore, LMPC and NMPC controllers can deal with state and input constraints explicitly