Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

Rapid Orbital Motion Emulator (ROME): Kinematics Modeling and Control

Space missions design requires already tested and trusted control algorithms for spacecraft motion. Rapidly testing control algorithms at a low cost is essential. A novel robotic system that emulates orbital motion in a laboratory environment is presented. The system is composed of a six degree of freedom robotic manipulator fixed on top of an omnidirectional ground vehicle accompanied with onboard computer and sensors. The integrated mobile manipulator is used as a testbed to emulate and realize orbital motion and control algorithms. The kinematic relations of the ground vehicle, robotic manipulator and the coupled kinematics are derived. The system is used to emulate an orbit trajectory. The system is scalable and capable of emulating servicing missions, satellite rendezvous and chaser follower problems

Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities

Guidance and search algorithms for mobile robots: application and analysis within the context of urban search and rescue

Urban Search and Rescue is a dangerous task for rescue workers and for this reason the use of mobile robots to carry out the search of the environment is becoming common place. These robots are remotely operated and the search is carried out by the robot operator. This work proposes that common search algorithms can be used to guide a single autonomous mobile robot in a search of an environment and locate survivors within the environment. This work then goes on to propose that multiple robots, guided by the same search algorithms, will carry out this task in a quicker time. The work presented is split into three distinct parts. The first is the development of a nonlinear mathematical model for a mobile robot. The model developed is validated against a physical system. A suitable navigation and control system is required to direct the robot to a target point within an environment. This is the second part of this work. The final part of this work presents the search algorithms used. The search algorithms generate the target points which allow the robot to search the environment. These algorithms are based on traditional and modern search algorithms that will enable a single mobile robot to search an area autonomously. The best performing algorithms from the single robot case are then adapted to a multi robot case. The mathematical model presented in the thesis describes the dynamics and kinematics of a four wheeled mobile ground based robot. The model is developed to allow the design and testing of control algorithms offline. With the model and accompanying simulation the search algorithms can be quickly and repeatedly tested without practical installation. The mathematical model is used as the basis of design for the manoeuvring control algorithm and the search algorithms. This design process is based on simulation studies. In the first instance the control methods investigated are Proportional-Integral-Derivative, Pole Placement and Sliding Mode. Each method is compared using the tracking error, the steady state error, the rise time, the charge drawn from the battery and the ability to control the robot through a simple motion. Obstacle avoidance is also covered as part of the manoeuvring control algorithm. The final aspect investigated is the search algorithms. The following search algorithms are investigated, Lawnmower, Random, HillClimbing, Simulated Annealing and Genetic Algorithms. Variations on these algorithms are also investigated. The variations are based on Tabu Search. Each of the algorithms is investigated in a single robot case with the best performing investigated within a multi robot case. A comparison between the different methods is made based on the percentage of the area covered within the time available, the number of targets located and the time taken to locate targets. It is shown that in the single robot case the best performing algorithms have high random elements and some structure to selecting points. Within the multi robot case it is shown that some algorithms work well and others do not. It is also shown that the useable number of robots is dependent on the size of the environment. This thesis concludes with a discussion on the best control and search algorithms, as indicated by the results, for guiding single and multiple autonomous mobile robots. The advantages of the methods are presented, as are the issues with using the methods stated. Suggestions for further work are also presented

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals

Modelado cinemático y control de robots móviles con ruedas

La presente tesis doctoral aborda el modelado cinemático y control de robots móviles con ruedas. En concreto se profundiza en los siguientes temas: - Se plantea el modelado de una rueda genérica que incluye todos los tipos comunes: fija, orientable centrada, orientable descentrada (castor) y sueca (también denominada universal, Mecanum ó Ilon). - Se describe un procedimiento eficiente para generar modelos cinemáticos, basado en el concepto de espacio nulo, el cual se aplica posteriormente a un gran número de tipos de robots móviles. Todos estos modelos son caracterizados en cuanto a su precisión o transmisión de errores (isotropía). - Se deduce un novedoso planteamiento geométrico que establece la singularidad de cualquier modelo cinemático de cualquier robot con ruedas. Este planteamiento se aplica a todos los tipos de robots anteriores. - Se desarrolla el modelado dinámico del robot para, a través de tres sucesivas aproximaciones y de la caracterización de las fricciones en las ruedas, llegar a un modelado cinemático con deslizamiento. - Se plantea un esquema de control del robot con tres bucles de control anidados (dinámico, cinemático y de planificación) que es conceptualmente similar a los empleados en robots manipuladores. En particular se profundiza en el bucle cinemático de nivel medio e indirectamente en el de planificación, al caracterizar las referencias que puede seguir cada tipo de robot sin error. - Se presentan experiencias de comprobación de los algoritmos de modelado con deslizamiento y de control del robot, realizadas sobre una plataforma eléctrica industrial (carretilla industrial). - Finalmente se desarrollan dos soluciones para las aplicaciones de aparcamiento en paralelo, con pre-planificación y caracterización geométrica, y de seguimiento de línea por visión.Gracia Calandin, LI. (2006). Modelado cinemático y control de robots móviles con ruedas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1840Palanci

Modeling of Slip for Wheeled Mobile Robots

Wheeled Mobile Robots (WMRs) are known to be nonholonomic systems, and most dynamic models of WMRs assume that the wheels undergo rolling without slipping. This paper deals with the problem of modeling and simulation of motion of a WMR when the conditions for rolling are not satisfied at the wheels. We use a traction model where the adhesion coefficient between the wheels of a WMR and a hard flat surface is a function of the wheel slip. This traction model is used in conjunction with the dynamic equations of motion to simulate the motion of the WMR. The simulations show that controllers which do not take into account wheel slip give poor tracking performance for the WMR and path deviation is small only for large adhesion coefficients. This work shows the importance of wheel slip and suggests use of accurate traction models for improving tracking performance of a WMR