Browsing by Subject "Robots--Control systems."
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Item Open Access 3D dynamic modeling of spherical wheeled self-balancing mobile robot(Bilkent University, 2012) İnal, Ali NailIn recent years, dynamically stable platforms that move on spherical wheels, also known as BallBots, gained popularity in the robotics literature as an alternative locomotion method to statically stable wheeled mobile robots. In contrast to wheeled platforms which do not have to explicitly be concerned about their balance, BallBot platforms must be informed about their dynamics and actively try to maintain balance. Up until now, such platforms have been approximated by simple planar models, with extensions to three dimensions through the combination of decoupled models in orthogonal sagittal planes. However, even though capturing certain aspects of the robot’s motion is possible with such decoupled models, they cannot represent inherently spatial aspects of motion such as yaw rotation or coupled inertial effects due to the motion of the rigid body. In this thesis, we introduce a novel, fully-coupled 3D model for such spherical wheeled balancing platforms. We show that our novel model captures important spatial aspects of motion that have previously not been captured by planar models. Moreover, our new model provides a better basis for controllers that are informed by more expressive system dynamics. In order to establish the expressivity and accuracy of this new model, we present simulation studies in dynamically rich situations. We use circular paths to reveal the advantages of the new model for fast maneuvers. Additionally, we introduce new inverse-dynamics controllers for a better attitude control and investigate within simulations the capability of sustaining dynamic behaviors. We study the relation between circular motions in attitude angles and associated motions in positional variables for BallBot locomotion.Item Open Access An actuated flexible spinal mechanism for a bounding quadrupedal robot(Bilkent University, 2012) Çulha, UtkuEvolution and experience based learning have given animals body structures and motion capabilities to survive in the nature by achieving many complicated tasks. Among these animals, legged vertebrates use their musculoskeletal bodies up to the limits to achieve actions involving high speeds and agile maneuvers. Moreover the flexible spine plays a very important role in providing auxiliary power and dexterity for such dynamic behaviors. Robotics research tries to imitate such dynamic abilities on mechanical platforms. However, most existing robots performing these dynamic motions does not include such a flexible spine architecture. In this thesis we investigate how quadrupedal bounding can be achieved with the help of an actuated flexible spine. Depending upon biological correspondences we first present a novel quadruped robot model with an actuated spine and relate it with our proposed new bounding gait controller model. By optimizing our model and a standard stiff backed model via repetitive parametric methods, we investigate the role of spinal actuation on the performance enhancements of the flexible model. By achieving higher ground speeds and hopping heights we discuss the relations between flexible body structure and stride properties of a dynamic bounding gait. Furthermore, we present an analytical model of the proposed robot structure along with the spinal architecture and analyze the dynamics and active forces on the overall system. By gathering simulation results we question how such a flexible spine system can be improved to achieve higher performances during dynamic gaits.Item Open Access Adaptive control of a one-legged hopping robot through dynamically embedded spring-loaded inverted pendulum template(Bilkent University, 2011) Uyanık, İsmailPractical realization of model-based dynamic legged behaviors is substantially more challenging than statically stable behaviors due to their heavy dependence on second-order system dynamics. This problem is further aggravated by the dif- ficulty of accurately measuring or estimating dynamic parameters such as spring and damping constants for associated models and the fact that such parameters are prone to change in time due to heavy use and associated material fatigue. In the first part of this thesis, we present an on-line, model-based adaptive control method for running with a planar spring-mass hopper based on a once-per-step parameter correction scheme. Our method can be used both as a system identifi- cation tool to determine possibly time-varying spring and damping constants of a miscalibrated system, or as an adaptive controller that can eliminate steady-state tracking errors through appropriate adjustments on dynamic system parameters. We use Spring-Loaded Inverted Pendulum (SLIP) model, which is the mostly used, effective and accurate descriptive tool for running animals of different sizes and morphologies, to evaluate our algorithm. We present systematic simulation studies to show that our method can successfully accomplish both accurate tracking and system identification tasks on this model. Additionally, we extend our simulations to Torque-Actuated Dissipative Spring-Loaded Inverted Pendulum (TD-SLIP) model towards its implementation on an actual robot platform. In the second part of the thesis, we present the design and construction of a onelegged hopping robot we built to test the practical applicability of our adaptive control algorithm. We summarize the mechanical, electronics and software design of our robot as well as the performed system identification studies to calibrate the unknown system parameters. Finally, we investigate the robot’s motion achieved by a simple torque-actuated open loop controller.Item Open Access Detection of tree trunks as visual landmarks in outdoor environments(Bilkent University, 2010) Yıldız, TuğbaOne of the basic problems to be addressed for a robot navigating in an outdoor environment is the tracking of its position and state. A fundamental first step in using algorithms for solving this problem, such as various visual Simultaneous Localization and Mapping (SLAM) strategies, is the extraction and identification of suitable stationary “landmarks” in the environment. This is particularly challenging in the outdoors geometrically consistent features such as lines are not frequent. In this thesis, we focus on using trees as persistent visual landmark features in outdoor settings. Existing work to this end only uses intensity information in images and does not work well in low-contrast settings. In contrast, we propose a novel method to incorporate both color and intensity information as well as regional attributes in an image towards robust of detection of tree trunks. We describe both extensions to the well-known edge-flow method as well as complementary Gabor-based edge detection methods to extract dominant edges in the vertical direction. The final stages of our algorithm then group these vertical edges into potential tree trunks using the integration of perceptual organization and all available image features. We characterize the detection performance of our algorithm for two different datasets, one homogeneous dataset with different images of the same tree types and a heterogeneous dataset with images taken from a much more diverse set of trees under more dramatic variations in illumination, viewpoint and background conditions. Our experiments show that our algorithm correctly finds up to 90% of trees with a false-positive rate lower than 15% in both datasets. These results establish that the integration of all available color, intensity and structure information results in a high performance tree trunk detection system that is suitable for use within a SLAM framework that outperforms other methods that only use image intensity information.Item Open Access Identification and stability analysis of periodic motions for a planar legged runner with a rigid body and a compliant leg(Bilkent University, 2013) Bayır, GüneşThe Spring-Loaded Inverted Pendulum (SLIP) model is an extensively used and fundamental template for modeling human and animal locomotion. Despite its wide use, the SLIP is a very simple model and considering the effects of body dynamics only as a point mass. Although the assumption of a point mass for the upper body simplifies system dynamics, it prevents us from performing detailed analysis for more realistic robot platforms with upper trunks. Hence, we consider an extension to the classic SLIP model to include the upper body dynamics in order to better understand human and animal locomotion. Due to its coupled rotational dynamics, extending the SLIP model to the Body-Attached Spring-Loaded Inverted Pendulum (BA-SLIP) brings additional difficulties in the analysis process, making it more difficult to obtain analytical solutions. Consequently, simulations have been used to reveal the periodic structure behind locomotion with this model, and to find fixed points of discretized system dynamics. These fixed points correspond to periodic motions of the system and are important in designing controllers since they are used as steady-state control targets for most applications. The main concern of this thesis is to find fixed points of the BA-SLIP model and to investigate the dimension of the fixed point manifold. We performed extensive simulation studies to find fixed points of the system and the properties of the underlying space with a PD controller. Our simulations revealed the existence of periodic gaits, in which the upper body should be downward oriented for stable locomotion. Additionally, a region of stability is found such that the model sustains periodic gaits when it stays inside this region. Finally, we show that fixed points for running with upright body orientation are unstable when system dynamics are regulated with a constant parameter controller. We also present some simulation results which indicate the existence of stable periodic motions when controllers with time varying parameters, that use current state information, are used.Item Open Access Improving visual SLAM by filtering outliers with the aid of optical flow(Bilkent University, 2011) Özaslan, TolgaSimultaneous Localization and Mapping (SLAM) for mobile robots has been one of the challenging problems for the robotics community. Extensive study of this problem in recent years has somewhat saturated the theoretical and practical background on this topic. Within last few years, researches on SLAM have been headed towards Visual SLAM, in which camera is used as the primary sensor. Superior to many SLAM application run with planar robots, VSLAM allows us to estimate the 3D model of the environment and 6-DOF pose of the robot. Being applied to robotics only recently, VSLAM still has a lot of room for improvement. In particular, a common issue both in normal and Visual SLAM algorithms is the data association problem. Wrong data association either disturbs stability or result in divergence of the SLAM process. In this study, we propose two outlier elimination methods which use predicted feature location error and optical flow field. The former method asserts estimated landmark projection and its measurement locations to be close. The latter accepts optical flow field as a reference and compares the vector formed by consecutive matched feature locations; eliminates matches contradicting with the local optical flow vector field. We have shown these two methods to be saving VSLAM from divergence and improving its overall performance. We have also described our new modular SLAM library, SLAM++.Item Open Access Model based methods for the control and planning of running robots(Bilkent University, 2009) Arslan, ÖmürThe Spring-Loaded Inverted Pendulum (SLIP) model has long been established as an effective and accurate descriptive model for running animals of widely differing sizes and morphologies. Not surprisingly, the ability of such a simple spring-mass model to capture the essence of running motivated several hopping robot designs as well as the use of the SLIP model as a control target for more complex legged robot morphologies. Further research on the SLIP model led to the discovery of several analytic approximations to its normally nonintegrable dynamics. However, these approximations mostly focus on steady-state running with symmetric trajectories due to their linearization of gravitational effects, an assumption that is quickly violated for locomotion on more complex terrain wherein transient, non-symmetric trajectories dominate. In the first part of the thesis , we introduce a novel gravity correction scheme that extends on one of the more recent analytic approximations to the SLIP dynamics and achieves good accuracy even for highly non-symmetric trajectories. Our approach is based on incorporating the total effect of gravity on the angular momentum throughout a single stance phase and allows us to preserve the analytic simplicity of the approximation to support research on reactive footstep planning for dynamiclegged locomotion. We compare the performance of our method with two other existing analytic approximations by simulation and show that it outperforms them for most physically realistic non-symmetric SLIP trajectories while maintaining the same accuracy for symmetric trajectories. Additionally, this part of the thesis continues with analytical approximations for tunable stiffness control of the SLIP model and their motion prediction performance analysis. Similarly, we show performance improvement for the variable stiffness approximation with gravity correction method. Besides this, we illustrate a possible usage of approximate stance maps for the controlling of the SLIP model. Furthermore, the main driving force behind research on legged robots has always been their potential for high performance locomotion on rough terrain and the outdoors. Nevertheless, most existing control algorithms for such robots either make rigid assumptions about their environments (e.g flat ground), or rely on kinematic planning with very low speeds. Moreover, the traditional separation of planning from control often has negative impact on the robustness of the system against model uncertainty and environment noise. In the second part of the thesis, we introduce a new method for dynamic, fully reactive footstep planning for a simplified planar spring-mass hopper, a frequently used dynamic model for running behaviors. Our approach is based on a careful characterization of the model dynamics and an associated deadbeat controller, used within a sequential composition framework. This yields a purely reactive controller with a very large, nearly global domain of attraction that requires no explicit replanning during execution. Finally, we use a simplified hopper in simulation to illustrate the performance of the planner under different rough terrain scenarios and show that it is robust to both model uncertainty and measurement noise.Item Open Access Motion planning of a mechanical snake using neural networks(Bilkent University, 1998) Fidan, BarışIn this thesis, an optimal strategy is developed to get a mechanical snake (a robot composed of a sequence of articulated links), which is located arbitrarily in an enclosed region, out of the region through a specified exit without violating certain constraints. This task is done in two stages: Finding an optimal path that can be tracked, and tracking the optimal path found. Each stage is implemented by a neural network. Neural network of the second stage is constructed by direct evaluation of the weights after designing an efficient structure. Two independent neural networks are designed to implement the first stage, one trained to implement an algorithm we have derived to generate minimal paths and the other trained using multi-stage neural network approach. For the second design, the intuitive multi-stage neural network back propagation approach in the literature is formalized.Item Open Access Simultaneous localization and mapping for unmanned aerial vehicles(Bilkent University, 2008) Kök, MehmetMost mobile robot applications require the robot to be able to localize itself in an unknown environment without prior information so that the robot can navigate and accomplish tasks. The robot must be able to build a map of the unknown environment while simultaneously localizing itself in this environment. The Simultaneous Localization and Mapping (SLAM) is the formulation of this problem which has drawn a considerable amount of interest in robotics research for the past two decades. This work focuses on the SLAM problem for single and multiple agents equipped with vision sensors. We develop a vision-based 2-D SLAM algorithm for single and multiple Unmanned Aerial Vehicles (UAV) flying at constant altitude. Using the features of images obtained from an on-board camera to identify different landmarks, we apply different approaches based on the Extended Kalman Filter (EKF), the Information Filter (IF) and the Particle Filter (PF) to the SLAM problem. We present some simulation results and provide a comparison between the different implementations. We find Particle Filter implementations to perform better in estimations when compared to EKF and IF, however EKF and IF present more consistent results.Item Open Access Smith predictor based controller design for a flexible robot arm(Bilkent University, 2013) Taşdelen, UğurIn this thesis, a new Smith predictor based controller is proposed for a flexible robot arm. A typical robot arm model includes high order modes with integral action from torque input to velocity output. Here we can also consider the effect of possible delays between the plant and the controller. The controller structure considered has an extended Smith predictor form. The designs use controller parametrization for stability and they also achieve certain performance objectives via interpolation conditions based on the disturbance rejection and setpoint tracking properties. This parametrization method allows widest freedom in controller parameters and this results in improved performance, both in set-point response and disturbance rejection. Free parameters in the controller determines the location of closed-loop poles. A hierarchical structure is used to extend Smith predictor structure to the position control loop. By protecting proposed structure, different approaches are shown to control the position. Compared to existing Smith predictor based designs, disturbance attenuation property with respect to periodic disturbances at a known frequency is improved. A two-degree of freedom controller structure is shown to be helpful in shaping the transient response under constant reference inputs. Stability robustness properties of this system are also investigated. Simulation results demonstrate the effectiveness of the proposed controller.Item Open Access Tracking and regulation control of a two-degree-of-freedom robot arm(Bilkent University, 2012) Güler, SametIn this thesis, servomechanism synthesis for a two-degree-of-freedom (2-DOF) serial chain revolute-revolute joint robot arm that achieves internal stability, reference signal tracking, and torque disturbance regulation is considered. We first derive the dynamic equations of the robot arm with Euler-Lagrange method by ignoring the effects of friction. Then, using system identification methods, we derive a plant model based on data obtained from a real system through experiments. We then employ PD and PID controllers along with the gravity compensation method to stabilize the system using the passivity properties. Alternatively, we linearize the system model and examine the performance of the same controllers. A linear controller is synthesized by invoking the internal model principle and is directly applied to the nonlinear plant model. The proposed fifth order linear controllers at each channel of the robot arm suffices to achieve not only tracking of step, ramp, and sinusoidal signals at one frequency, but also regulation of step, ramp, and sinusoidal disturbances. It is shown via simulations that, even though the plant model is nonlinear, the synthesized linear controller performs better than the commonly used PID controllers.Item Open Access Using shape information from natural tree landmarks for improving SLAM performance(Bilkent University, 2012) Turan, BilalLocalization and mapping are crucial components for robotic autonomy. However, such robots must often function in remote, outdoor areas with no a-priori knowledge of the environment. Consequently, it becomes necessary for field robots to be able to construct their own maps based on exteroceptive sensor readings. To this end, visual sensing and mapping through naturally occurring landmarks have distinct advantages. With the availability of high bandwidth data provided by visual sensors, meaningful and uniquely identifiable objects can be detected. This improves the construction of maps consisting of natural landmarks that are meaningful for human readers as well. In this thesis, we focus on the use of trees in an outdoor environment as a suitable set of landmarks for Simultaneous Localization and Mapping (SLAM). Trees have a relatively simple, near vertical structure which makes them easily and consistently detectable. Furthermore, the thickness of a tree can be accurately determined from different viewpoints. Our primary contribution is the usage of the width of a tree trunk as an additional sensory reading, allowing us to include the radius of tree trunks on the map. To this end, we introduce a new sensor model that relates the width of a tree landmark on the image plane to the radius of its trunk. We provide a mathematical formulation of this model, derive associated Jacobians and incorporate our sensor model into a working EKF SLAM implementation. Through simulations we show that the use of this new sensory reading improves the accuracy of both the map and the trajectory estimates without additional sensor hardware other than a monocular camera.