Browsing by Subject "Robots--Programming."
Now showing 1 - 4 of 4
Results Per Page
Sort Options
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 A backwards theorem prover with focusing, resource management and constraints for robotic planning within intuitionistic linear logic(Bilkent University, 2010) Kortik, SıtarThe main scope of this thesis is implementing a backwards theorem prover with focusing, resource management and constraints within the intuitionistic first-order linear logic for robotic planning problems. To this end, backwards formulations provide a simpler context for experimentation. However, existing backward theorem provers are either implemented without regard to the efficiency of the proofsearch, or when they do, restrict the language to smaller fragments such as Linear Hereditary Harrop Formulas (LHHF). The former approach is unsuitable since it significantly impairs the scalability of the resulting system. The latter family of theorem provers address the scalability issue but impact the expressivity of the resulting language and may not be able to deal with certain non-deterministic planning elements. The proof theory we describe in this thesis enables us to effectively experiment with the use of linearity and continuous constraints to encode dynamic state elements characteristic of robotic planning problems. To this end, we describe a prototype implementation of our system in SWI-Prolog, and also incorporate continuous constraints into the prototype implementation of the system. We support the expressivity and efficiency of our system with some examples.Item Open Access Experiments in integrating constraints with logical reasoning for robotic planning within the twelf logical framework and the prolog language(Bilkent University, 2008) Duatepe, MertThe underlying domain of various application areas, especially real-time systems and robotic applications, generally includes a combination of both discrete and continuous properties. In robotic applications, a large amount of different approaches are introduced to solve either a discrete planning or control theoretic problem. Only a few methods exist to solve the combination of them. Moreover, these methods fail to ensure a uniform treatment of both aspects of the domain. Therefore, there is need for a uniform framework to represent and solve such problems. A new formalism, the Constrained Intuitionistic Linear Logic (CILL), combines continuous constraint solvers with linear logic. Linear logic has a great property to handle hypotheses as resources, easily solving state transition problems. On the other hand, constraint solvers deal well with continuous problems defined as constraints. Both properties of CILL gives us powerful ways to express and reason about the robotics domain. In this thesis, we focus on the implementation of CILL in both the Twelf Logical Framework and Prolog. The reader of this thesis can find answers of why classical aspects are not proper for the robotics domain, what advantages one can gain from intuitionism and linearity, how one can define a simple robotic domain in a logical formalism, how a proof in logical system corresponds to a plan in the robotic domain, what the advantages and disadvantages of logical frameworks and Prolog have and how the implementation of CILL can or cannot be done using both Twelf Logical Framework and Prolog.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.