Adaptive robust control of an omnidirectional mobile platform for autonomous service robots in polar coordinates

This paper presents an adaptive robust control method for trajectory tracking and path following of an omni-directional wheeled mobile platform with actuators' uncertainties. The polar-space kinematic model of the platform with three independent driving omnidirectional wheels equally spaced at 120 from one another is briefly introduced, and the dynamic models of the three uncertain servomotors mounted on the driving wheels are also described. With the platform's kinematic model and the motors' dynamic model associated two unknown parameters, the adaptive robust controller is synthesized via the integral backstepping approach. Computer simulations and experimental results are conducted to show the effectiveness and merits of the proposed control method in comparison with a conventional PI feedback control method

Fault detection in trajectory tracking of wheeled mobile robots

The problem of fault detection in nonlinear systems with application to trajectory tracking of nonholonomic wheeled mobile robots (WMRs) is addressed in this thesis. For the considered application, a nonholonomic wheeled mobile robot--having nonlinear kinematics--is required to follow a predefined smooth trajectory (in the absence of obstacles in the environment). This goal has to be accomplished despite the presence of faults that may occur in two of its major subsystems which are vital for navigation, namely the driving subsystem and the steering subsystem. These faults are assumed to be caused by actuator faults in either of these two subsystems. The problem addressed here is to detect the presence of faults and to determine the subsystem which has been affected by these faults. Toward this end, two different fault detection approaches are proposed and investigated. The first approach is based on system identification through Extended Kalman Filters (EKF) whereas the second one is based on system identification via artificial neural networks. In the former approach a novel method for residual generation is proposed while in the latter by utilizing the neural network's formal stability properties the desired performance can be guaranteed. Each of the proposed fault detection methods is studied subject to two different kinds of controllers (namely a dynamic linear controller and a dynamic feedback linearization based controller) and two different types of actuator faults (namely the Loss-of-Effectiveness fault and Locked-In-Place fault). In this way, the impact of the controller strategy on the fault detection approach is also investigated and evaluated

Control of Outdoor Robots at Higher Speeds on Challenging Terrain

This thesis studies the motion control of wheeled mobile robots. Its focus is set on high speed control on challenging terrain. Additionally, it deals with the general problem of path following, as well as path planning and obstacle avoidance in difficult conditions. First, it proposes a heuristic longitudinal control for any wheeled mobile robot, and evaluates it on different kinematic configurations and in different conditions, including laboratory experiments and participation in a robotic competition. Being the focus of the thesis, high speed control on uneven terrain is thoroughly studied, and a novel control law is proposed, based on a new model representation of skid-steered vehicles, and comprising of nonlinear lateral and longitudinal control. The lateral control part is based on the Lyapunov theory, and the convergence of the vehicle to the geometric reference path is proven. The longitudinal control is designed for high speeds, taking actuator saturation and the vehicle properties into account. The complete solution is experimentally tested on two different vehicles on several different terrain types, reaching the speeds of ca. 6 m/s, and compared against two state-of-the-art algorithms. Furthermore, a novel path planning and obstacle avoidance system is proposed, together with an extension of the proposed high speed control, which builds up a navigation system capable of autonomous outdoor person following. This system is experimentally compared against two classical obstacle avoidance methods, and evaluated by following a human jogger in outdoor environments, with both static and dynamic obstacles. All the proposed methods, together with various different state-of-the-art control approaches, are unified into one framework. The proposed framework can be used to control any wheeled mobile robot, both indoors and outdoors, at low or high speeds, avoiding all the obstacles on the way. The entire work is released as open-source software

Sliding mode control applied in trajectory-tracking of WMRs and autonomous vehicles

Tese de doutoramento apresentada à Fac. de Ciências e Tecnologia da Universidade de CoimbraThe thesis is structured as follows: • Chapter 2: Trajectory tracking problems are summarized. • Chapter 3: Kinematic and dynamic modeling of theWMRs and car-like robots are presented. • Chapter 4: The concept of sliding mode are first introduced. Then the fundamentals of SMC are summarized, including basic definitions, methods of sliding surface and control law design, robustness properties and the methods on handling chattering problems. New sliding-mode trajectory-tracking and slidingmode path-following controllers for WMRs and car-like vehicles, are also proposed in this chapter. • Chapter 5: The trajectory/path planning are developed, including the velocity profile. • Chapter 6: A model with two freedom degrees is considered for the HNC model. The user comfort is examined not only in the time domain, but also in the frequency domain. • Chapter 7: Experimental results obtained with the implementation of the proposed controllers in RobChair are summarized and discussed. • Chapter 8: Finally, conclusions are drawn and some suggestions for future work are provided

Graceful Navigation for Mobile Robots in Dynamic and Uncertain Environments.

The ability to navigate in everyday environments is a fundamental and necessary skill for any autonomous mobile agent that is intended to work with human users. The presence of pedestrians and other dynamic objects, however, makes the environment inherently dynamic and uncertain. To navigate in such environments, an agent must reason about the near future and make an optimal decision at each time step so that it can move safely toward the goal. Furthermore, for any application intended to carry passengers, it also must be able to move smoothly and comfortably, and the robot behavior needs to be customizable to match the preference of the individual users. Despite decades of progress in the field of motion planning and control, this remains a difficult challenge with existing methods. In this dissertation, we show that safe, comfortable, and customizable mobile robot navigation in dynamic and uncertain environments can be achieved via stochastic model predictive control. We view the problem of navigation in dynamic and uncertain environments as a continuous decision making process, where an agent with short-term predictive capability reasons about its situation and makes an informed decision at each time step. The problem of robot navigation in dynamic and uncertain environments is formulated as an on-line, finite-horizon policy and trajectory optimization problem under uncertainty. With our formulation, planning and control becomes fully integrated, which allows direct optimization of the performance measure. Furthermore, with our approach the problem becomes easy to solve, which allows our algorithm to run in real time on a single core of a typical laptop with off-the-shelf optimization packages. The work presented in this thesis extends the state-of-the-art in analytic control of mobile robots, sampling-based optimal path planning, and stochastic model predictive control. We believe that our work is a significant step toward safe and reliable autonomous navigation that is acceptable to human users.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120760/1/jongjinp_1.pd

Motion planning and stabilization of nonholonomic systems using gradient flow approximations

Nonlinear control-affine systems with time-varying vector fields are considered in the paper. We propose a unified control design scheme with oscillating inputs for solving the trajectory tracking and stabilization problems. This methodology is based on the approximation of a gradient like dynamics by trajectories of the designed closed-loop system. As an intermediate outcome, we characterize the asymptotic behavior of solutions of the considered class of nonlinear control systems with oscillating inputs under rather general assumptions on the generating potential function. These results are applied to examples of nonholonomic trajectory tracking and obstacle avoidance.Comment: submitte

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches