Tire/Road Contact Condition Identification Using Algebraic Numerical Differentiation

International audienceIn this paper, a realistic simulation model for Wheeled Mobile Robot (WMR) is given by a dynamical system that switches between three models corresponding to three different tire/road contact conditions: ideal condition, skidding condition and slipping condition. Then, an algebraic based numerical identification for the discrete state (tire/road contact condition) of this switching system is proposed. Finally, specific estimators for the uncertain parameters encountered in the identification scheme are given

Neural Network Controller Design for a Mobile Robot Navigation; a Case Study

Mobile robot are widely applied in various aspect of human  life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task  when applied autonomously in dynamic and uncertain environment. The  ap- plication of artificial intelligence, namely neural   network,  can provide a ”brain” for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be  reduced by choosing the right model of the system, either   from mathematical modeling or directly taken from the input of sensory data  information. In this study, we compare the presented methods of previous  researches that applies neural network to mobile robot navigation. The comparison  is started  by considering  the right  mathematical model for the robot, getting the Jacobian  matrix  for online training, and giving the achieved input model to  the designed neural network layers in order to get the estimated position of the robot. From this literature study, it  is concluded that the consideration of both kinematics and dynamics modeling  of the robot will result in better performance since the exact parameters of the system are known

Mobile robot control on uneven and slippery ground: An adaptive approach based on a multi-model observer

International audienceThis paper proposes an algorithm dedicated to off-road mobile robot path tracking at high speed. In order to ensure a high accuracy, a predictive and adaptive approach is developed to face the various perturbations due to this context (mainly the bad grip conditions and the terrain geometry). The control law is based on previous work, and requires the knowledge of sideslip angles, which cannot be directly measured. As a result, an observer based on two levels of modeling (kinematic and dynamic) is proposed to ensure a relevant and fast estimation. If the kinematic part is independent from the terrain geometry, the dynamic model used in this paper requires to take explicitly into account the influence of the terrain geometry on mobile robot dynamic. It is achieved by the introduction of the lateral robot inclination, which is on-line estimated via a kalman filter and integrated in the dynamical model. The advantages of the proposed contribution to path tracking control are investigated through full-scale experiments achieved at high speed (up to 6m/s) on an uneven and grass field

Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots

A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off-road mobile robots

Automation in outdoor applications (farming, surveillance, military activities, etc.) requires highly accurate control of mobile robots, at high speed, although they are moving on low-grip terrain. To meet such expectations, advanced control laws accounting for natural ground specificities (mainly sliding effects) must be derived. In previous work, adaptive and predictive control algorithms, based on an extended kinematic representation, have been proposed. Satisfactory experimental results have been reported (accurate to within ±10 cm, whatever the grip conditions), but at limited velocity (below 3 m·s-1). Nevertheless, simulations reveal that control accuracy is decreased when vehicle speed is increased (up to 10 m·s-1). In particular, oscillations are observed at curvature transition. This drawback is due to delays in sideslip angle estimation, unavoidable at high speed because only an extended kinematic representation was used. In this paper, a mixed backstepping kinematic and dynamic observer is designed to improve observation of these variables: the slow-varying data are still estimated from a kinematic representation, which is then injected into a dynamic observer to supply reactive and reliable sliding variable (namely sideslip angle) estimation, without increasing the noise level. The algorithm is evaluated via advanced simulations (coupling Adams and MatLab software) investigating high-speed capabilities. Actual experiments at lower speed (experimental platform maximum velocity) demonstrate the benefits of the proposed approach

Pengontrolan Penjejak Dinding dengan Batasan Orientasi pada Kursi Roda Robotik

Pada makalah ilmiah ini disajikan desain sistem kontrol penjejak dinding pada kursi roda robotik dengan keterbatasan pada pembacaan sensor. Rangkaian sensor ultrasonik digunakan untuk menentukan jarak dan sudut orientasi dari kursi roda robotik terhadap dinding yang menjadi acuan. Algoritma kontrol diturunkan menggunakan fungsi Lyapunov Barrier untuk menjamin kestabilan asimtotik dari sistem dengan batasan sudut pembacaan sensor ultrasonik. Hasil simulasi dari sistem kontrol memperlihatkan kursi roda robotik dapat bergerak dengan jarak yang diinginkan dari dinding dengan mempertahankan sudut orientasi tidak melebihi batasan pembacaan sensor ultrasonik

Interactive multiple model filtering for robotic navigation and tracking applications

The work contained in this thesis focuses on two main objectives. The first objective is to evaluate the Interactive Multiple Model (IMM) filter method for robotic applications including inertial navigation systems (INS) and computer vision tracking. The second objective is to design an experimental testbed for multi-model mobile robot state estimation research in the Intelligent Systems Laboratory (ISLAB) at Memorial University. An IMM estimator uses multiple filters that run simultaneously to produce a combined weighted estimation of an observed system’s states. The weights are functions of the likelihood of how well each individual filter matches the current behaviour exhibited by the system. The performance of IMM filtering is evaluated using two different strategies for augmenting the system’s filter banks. The first method uses multiple kinematic models (constant velocity and constant acceleration models) in a mean-shift-based computer vision tracking application. The results of this experiment indicate that the IMM improves tracking performance due to its ability to adapt to the continuously changing motion characteristics of 2D blobs in videos. The second approach uses the same kinematics for each filter; however, the process and sensor noise parameters are tuned differently for each model. This method is tested in INS applications for both an automobile and a skid-steer mobile robot (Seekur Jr). Results show that the method improves INS tracking over single model Extended Kalman Filter (EKF) designs. Furthermore, an augmented state-space model containing skid-steer instantaneous center of rotation (ICR) kinematics is presented for future testing on the Seekur Jr INS. The experimental testbed designed in this thesis work is an operational data acquisition system developed for use with the Seekur Jr robot. The Seekur Jr platform has been Robot Operating System (ROS) enabled with access to data streams from 2D Lidar, 3D nodding Lidar, inertial measurement unit, digital compass, wheel encoder, onboard Global Positioning System (GPS), real-time kinematic (RTK) differential global positioning system (DGPS) ground truth, and vision sensors. The physical setup and data networking aspects of the testbed have been used for validation of an IMM filter presented in this thesis and is fully configured for future multi-model localization experiments of the ISLAB

Design, testing and validation of model predictive control for an unmanned ground vehicle

The rapid increase in designing, manufacturing, and using autonomous robots has attracted numerous researchers and industries in recent decades. The logical motivation behind this interest is the wide range of applications. For instance, perimeter surveillance, search and rescue missions, agriculture, and construction. In this thesis, motion planning and control based on model predictive control (MPC) for unmanned ground vehicles (UGVs) is tackled. In addition, different variants of MPC are designed, analysed, and implemented for such non-holonomic systems. It is imperative to focus on the ability of MPC to handle constraints as one of the motivations. Furthermore, the proliferation of computer processing enables these systems to work in a real-time scenario. The controller's responsibility is to guarantee an accurate trajectory tracking process to deal with other specifications usually not considered or solved by the planner. However, the separation between planner and controller is not necessarily defined uniquely, even though it can be a hybrid process, as seen in part of this thesis. Firstly, a robust MPC is designed and implemented for a small-scale autonomous bulldozer in the presence of uncertainties, which uses an optimal control action and a feed-forward controller to suppress these uncertainties. More precisely, a linearised variant of MPC is deployed to solve the trajectory tracking problem of the vehicle. Afterwards, a nonlinear MPC is designed and implemented to solve the path-following problem of the UGV for masonry in a construction context, where longitudinal velocity and yaw rate are employed as control inputs to the platform. For both the control techniques, several experiments are performed to validate the robustness and accuracy of the proposed scheme. Those experiments are performed under realistic localisation accuracy, provided by a typical localiser. Most conspicuously, a novel proximal planning and control strategy is implemented in the presence of skid-slip and dynamic and static collision avoidance for the posture control and tracking control problems. The ability to operate in moving objects is critical for UGVs to function well. The approach offers specific planning capabilities, able to deal at high frequency with context characteristics, which the higher-level planner may not well solve. Those context characteristics are related to dynamic objects and other terrain details detected by the platform's onboard perception capabilities. In the control context, proximal and interior-point optimisation methods are used for MPC. Relevant attention is given to the processing time required by the MPC process to obtain the control actions at each actual control time. This concern is due to the need to optimise each control action, which must be calculated and applied in real-time. Because the length of a prediction horizon is critical in practical applications, it is worth looking into in further detail. In another study, the accuracies of robust and nonlinear model predictive controllers are compared. Finally, a hybrid controller is proposed and implemented. This approach exploits the availability of a simplified cost-to-go function (which is provided by a higher-level planner); thus, the hybrid approach fuses, in real-time, the nominal CTG function (nominal terrain map) with the rest of the critical constraints, which the planner usually ignores. The conducted research fills necessary gaps in the application areas of MPC and UGVs. Both theoretical and practical contributions have been made in this thesis. Moreover, extensive simulations and experiments are performed to test and verify the working of MPC with a reasonable processing capability of the onboard process

Kontrol Penjejak Dinding pada Kursi Roda Robotik dengan Batasan Pengukuran Sudut Orientasi dan Jarak

Pada makalah ilmiah ini disajikan desain sistem kontrol penjejak dinding pada kursi roda robotik dengan keterbatasan pembacaan sensor. Rangkaian sensor ultrasonik digunakan untuk menentukan jarak dan sudut orientasi dari kursi roda robotik terhadap dinding yang menjadi acuan. Algoritma kontrol diturunkan menggunakan fungsi Lyapunov  Barrier untuk menjamin kestabilan asimtotik dari sistem dengan batasan pengukuran sudut orientasi dan  jarak dari sensor ultrasonik. Hasil simulasi menunjukkan perbedaan antara algoritma kontrol yang menggunakan fungsi Barrier, dimana sudut orientasi dan jarak tidak keluar dari batasan kemampuan sensor ultrasonik, dan yang tidak, dimana sudut orientasi dan jarak dapat keluar dari batasan. Hasil eksperimen dari implementasi algoritma kontrol memperlihatkan kursi roda robotik dapat bergerak dengan jarak yang diinginkan dari dinding dengan mempertahankan jarak dan sudut orientasi tidak melebihi batasan kemampuan dari sensor ultrasonik

Diseño de un robot móvil autónomo de telepresencia

The recent rise in tele-operated autonomous mobile vehicles calls for a seamless control architecture that reduces the learning curve when the platform is functioning autonomously (without active supervisory control), as well as when tele-operated. Conventional robot plat-forms usually solve one of two problems. This work develops a mobile base using the Robot Operating System (ROS) middleware for teleoperation at low cost. The three-layer architec-ture introduced adds or removes operator complexity. The lowest layer provides mobility and robot awareness; the second layer provides usability; the upper layer provides inter-activity. A novel interactive control that combines operator intelligence/ skill with robot/autonomous intelligence enabling the mobile base to respond to expected events and ac-tively react to unexpected events is presented. The experiments conducted in the robot laboratory summarises the advantages of using such a system.El reciente auge de los vehículos móviles autónomos teleoperados exige una arquitectura de control sin fisuras que reduzca la curva de aprendizaje cuando la plataforma funciona de forma autónoma (sin control de supervisión activo), así como cuando es teleoperada. Las plataformas robóticas convencionales suelen resolver uno de los dos problemas. Este tra-bajo desarrolla una base móvil que utiliza el middleware Robot Operating System (ROS) para la teleoperación a bajo coste. La arquitectura de tres capas introducida añade o elimina la complejidad del operador. La capa más baja proporciona movilidad y conciencia robótica; la segunda capa proporciona usabilidad; la capa superior proporciona interactividad. Se presenta un novedoso control interactivo que combina la inteligencia/habilidades del op-erador con la inteligencia autónoma del robot, lo que permite que la base móvil responda a los eventos esperados y reaccione activamente a los eventos inesperados. Los experi-mentos realizados en el laboratorio robótica resumen las ventajas de utilizar un sistema de este tipoDepartamento de Ingeniería de Sistemas y AutomáticaMáster en Electrónica Industrial y Automátic