Nonlinear model predictive control optimization for autonomous mobile robots

In the Agent-Target Coordination field, one of the most researched area is the coordination of a group of heterogeneous mobile agents in order to accomplish advanced tasks. Thanks to the accessibility and the improvement of Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) over the last decades, their use in exploration, research and industrial cooperative applications is increasing. However, solving challenging tasks, such as {search\&rescue} and environmental monitoring, demands the application of control laws that require high performance computational systems. Despite the components miniaturization, the complexity of developing light-weight but performing processors has lead the growth of cloud-computing. In this thesis, it is addressed the problem of driving a UGV to follow an UAV in order to set up a landing scenario, while dealing with computational resources allocation. Specifically, the target trajectory is not know in advance by the agent and the only source of information concerning the poses and velocities of both vehicles comes from the camera attached to the external computational node. To solve this problem, it is proposed a cascade of control techniques based on the Model Predictive Control (MPC) and Gaussian Process Regression (GPR) approaches. The Model Predictive Control controller is devoted to solving the Agent-Target Coordination problem by driving the UGV under the aerial vehicle. The GPR module, instead, is dedicated to predicting the computational effort of the controller, to providing the MPC control invariant $N$ and to allocating the computation of the Model Predictive Control solution locally or on the external node. Simulation results on Matlab are presented in order to illustrate and validate the proposed approach.In the Agent-Target Coordination field, one of the most researched area is the coordination of a group of heterogeneous mobile agents in order to accomplish advanced tasks. Thanks to the accessibility and the improvement of Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) over the last decades, their use in exploration, research and industrial cooperative applications is increasing. However, solving challenging tasks, such as {search\&rescue} and environmental monitoring, demands the application of control laws that require high performance computational systems. Despite the components miniaturization, the complexity of developing light-weight but performing processors has lead the growth of cloud-computing. In this thesis, it is addressed the problem of driving a UGV to follow an UAV in order to set up a landing scenario, while dealing with computational resources allocation. Specifically, the target trajectory is not know in advance by the agent and the only source of information concerning the poses and velocities of both vehicles comes from the camera attached to the external computational node. To solve this problem, it is proposed a cascade of control techniques based on the Model Predictive Control (MPC) and Gaussian Process Regression (GPR) approaches. The Model Predictive Control controller is devoted to solving the Agent-Target Coordination problem by driving the UGV under the aerial vehicle. The GPR module, instead, is dedicated to predicting the computational effort of the controller, to providing the MPC control invariant $N$ and to allocating the computation of the Model Predictive Control solution locally or on the external node. Simulation results on Matlab are presented in order to illustrate and validate the proposed approach

Integration of fault tolerance and hardware redundancy techniques into the design of mobile platforms

This work addresses the development of a fault-tolerant mobile platform. Fault-tolerant mechanical system design is an emerging technology that attempts to build highly reliable systems by incorporating hardware and software architectures. For this purpose, previous work in fault-tolerant were reviewed. Alternate architectures were evaluated to maximize the fault tolerance capabilities of the driving and steering systems of a mobile platform. The literature review showed that most of the research work on fault tolerance has been done in the area of kinematics and control systems of robotic arms. Therefore, hardware redundancy and fault tolerance in mobile robots is an area to be researched. The prototype constructed as part of this work demonstrated basic principles and uses of a fault-tolerant mechanism, and is believed to be the first such system in its class. It is recommended that different driving and steering architectures, and the fault-tolerant controllers\u27 performance be tested on this prototype

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

Experiments in thrusterless robot locomotion control for space applications

While performing complex assembly tasks or moving about in space, a space robot should minimize the amount of propellant consumed. A study is presented of space robot locomotion and orientation without the use of thrusters. The goal was to design a robot control paradigm that will perform thrusterless locomotion between two points on a structure, and to implement this paradigm on an experimental robot. A two arm free flying robot was constructed which floats on a cushion of air to simulate in 2-D the drag free, zero-g environment of space. The robot can impart momentum to itself by pushing off from an external structure in a coordinated two arm maneuver, and can then reorient itself by activating a momentum wheel. The controller design consists of two parts: a high level strategic controller and a low level dynamic controller. The control paradigm was verified experimentally by commanding the robot to push off from a structure with both arms, rotate 180 degs while translating freely, and then to catch itself on another structure. This method, based on the computed torque, provides a linear feedback law in momentum and its derivatives for a system of rigid bodies

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Traction Modeling and Control of a Differential Drive Mobile Robot to Avoid Wheel Slip

The motion of a differential drive mobile robot with consideration of slip at contact between the wheels and the ground is studied in this work. Traction forces between the wheel and the ground are derived by considering a rigid wheel, rigid ground interaction model and a caster wheel which provides support to the mobile robot during motion. The motion governing equations are determined by incorporating the traction forces. Numerical simulations are conducted to learn the motion behavior of the robot with wheel slip for a range of wheel input torques. Based on the traction force model and observations from numerical simulations, a slip avoidance controller that limits the input torques is developed. Experiments are conducted to verify the characteristics of the dynamic model with slip and the control strategy used to avoid slip. Models that describe the dynamics of a differential drive mobile robot with and without slip are presented and discussed. A traction force model is developed by considering a simple Coulomb friction model. The caster wheel plays an important role in determining the traction forces. The longitudinal and lateral velocities of the wheel are used to compute the longitudinal and lateral forces. Wheel slip occurs if the reaction force exerted by the applied torque is greater than the static frictional force, which is calculated by the proposed model and this limit is used to implement a slip avoidance controller. Numerical simulations and experiments of the system using the proposed traction model reveal that the angular velocity of the wheels is greater than the corresponding linear velocity when slip occurs. The proposed torque limiting controller to avoid slip is also implemented in numerical simulations and experiments. Experimental results show a good correlation with the numerical simulations, thus verifying the approach and the developed dynamic model with wheel slip.Mechanical Engineerin

MODEL PREDICTIVE CONTROL OF SKID-STEERED MOBILE ROBOT WITH DEEP LEARNING SYSTEM DYNAMICS

This thesis project presents several model predictive control (MPC) strategies for control of skid-steered mobile robots (SSMRs) using two different combinations of software environment, optimization tool and machine learning framework. The control strategies are tested in WeBots simulator. Spatial-based path following MPC of SSMR with static obstacle avoidance is developed in MATLAB environment with ACADO optimization toolkit using spatial kinematic model of SSMR. It includes static obstacle and border avoidance strategy based on artificial potential fields. Simulations show that the controller is effective at driving SSMR on a track, while avoiding borders and obstacles. Several more MPCs are developed using Python environment, ACADOS optimisation framework, and Pytorch-Casadi integration framework. Two time-domain controllers are made in Python environment, one based on SSMR kinematic model and another based on data-driven state-space model using Pytorch- Casadi framework. Both are setup to reach a goal point in simulation experiment. Experiments show that both versions reliably reach a target point. Standard and data-driven versions of spatial path following MPC are developed. Standard is a reimplementation of MPC designed in MATLAB with modifications to cost function and border avoidance, without static obstacle avoidance. Data-driven path following MPC is an extension of standard variant with state-space model replaced with a hybrid of spatial kinematics and data-driven model. Simulation of both spatial controllers confirm their effectiveness in following reference path