Analytically-Guided Design of a Tailed Bipedal Hopping Robot

We present the first fully spatial hopping gait of a 12 DoF tailed biped driven by only 4 actuators. The control of this physical machine is built up from parallel compositions of controllers for progressively higher DoF extensions of a simple 2 DoF, 1 actuator template. These template dynamics are still not themselves integrable, but a new hybrid averaging analysis yields a conjectured closed form representation of the approximate hopping limit cycle as a function of its physical and control parameters. The resulting insight into the role of the machine\u27s kinematic and dynamical design choices affords a redesign leading to the newly achieved behavior

Modular Hopping and Running via Parallel Composition

Though multi-functional robot hardware has been created, the complexity in its functionality has been constrained by a lack of algorithms that appropriately manage flexible and autonomous reconfiguration of interconnections to physical and behavioral components. Raibert pioneered a paradigm for the synthesis of planar hopping using a composition of ``parts\u27\u27: controlled vertical hopping, controlled forward speed, and controlled body attitude. Such reduced degree-of-freedom compositions also seem to appear in running animals across several orders of magnitude of scale. Dynamical systems theory can offer a formal representation of such reductions in terms of ``anchored templates,\u27\u27 respecting which Raibert\u27s empirical synthesis (and the animals\u27 empirical performance) can be posed as a parallel composition. However, the orthodox notion (attracting invariant submanifold with restriction dynamics conjugate to a template system) has only been formally synthesized in a few isolated instances in engineering (juggling, brachiating, hexapedal running robots, etc.) and formally observed in biology only in similarly limited contexts. In order to bring Raibert\u27s 1980\u27s work into the 21st century and out of the laboratory, we design a new family of one-, two-, and four-legged robots with high power density, transparency, and control bandwidth. On these platforms, we demonstrate a growing collection of $\{$body, behavior$\}$ pairs that successfully embody dynamical running / hopping ``gaits\u27\u27 specified using compositions of a few templates, with few parameters and a great deal of empirical robustness. We aim for and report substantial advances toward a formal notion of parallel composition---embodied behaviors that are correct by design even in the presence of nefarious coupling and perturbation---using a new analytical tool (hybrid dynamical averaging). With ideas of verifiable behavioral modularity and a firm understanding of the hardware tools required to implement them, we are closer to identifying the components required to flexibly program the exchange of work between machines and their environment. Knowing how to combine and sequence stable basins to solve arbitrarily complex tasks will result in improved foundations for robotics as it goes from ad-hoc practice to science (with predictive theories) in the next few decades

A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability

For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the proble

Towards energy-efficient limit-cycle walking in biped service robots: design analysis, modeling and experimental study of biped robot actuated by linear motors

Researchers have been studying biped robots for many years, and, while many advances in the field have been accomplished, there still remain the challenge to transfer the existing solutions into real applications. The main issues are related to mobility and autonomy. In mobility, biped robots have evolved greatly, nevertheless they are still far from what a human can do in the work-site. Similarly, autonomy of biped platforms has been tackled on several different grounds, but its core problem still remains, and it is associated to energy issues. Because of these energy issues, lately the main attention has been redirected to the long term autonomy of the biped robotics platforms. For that, much effort has been made to develop new more energy-efficient biped robots. The GIMBiped project in Aalto University was established to tackle the previous issues in energy efficiency and mobility, through the study and implementation of dynamic and energy-efficient bipedal robotic waking. This thesis falls into the first studies needed to achieve the previous goal using the GIMBiped testbed, starting with a detailed analysis of the nonlinear dynamics of the target system, using a modeling and simulation tools. This work also presents an assessment of different control techniques based on Limit Cycle walking, carried out on a two-dimensional kneed bipedal simulator. Furthermore, a numerical continuation analysis of the mechanical parameters of the first GIMBiped prototype was performed, using the same approximated planar kneed biped model. This study is done to analyze the effect that such variations in the mechanical design parameters produce in the stability and energy-efficiency of the system.Finally, experiments were performed in the GIMBiped testbed. These experiments show the results of a hybrid control technique proposed by the author, which combines traditional ZMP-based walking approach with a Limit Cycle trajectory-following control. Furthermore the results of a pure ZMP-based type of control are also presented.

Improving Scalability of Evolutionary Robotics with Reformulation

Creating systems that can operate autonomously in complex environments is a challenge for contemporary engineering techniques. Automatic design methods offer a promising alternative, but so far they have not been able to produce agents that outperform manual designs. One such method is evolutionary robotics. It has been shown to be a robust and versatile tool for designing robots to perform simple tasks, but more challenging tasks at present remain out of reach of the method. In this thesis I discuss and attack some problems underlying the scalability issues associated with the method. I present a new technique for evolving modular networks. I show that the performance of modularity-biased evolution depends heavily on the morphology of the robot’s body and present a new method for co-evolving morphology and modular control. To be able to reason about the new technique I develop reformulation framework: a general way to describe and reason about metaoptimization approaches. Within this framework I describe a new heuristic for developing metaoptimization approaches that is based on the technique for co-evolving morphology and modularity. I validate the framework by applying it to a practical task of zero-g autonomous assembly of structures with a fleet of small robots. Although this work focuses on the evolutionary robotics, methods and approaches developed within it can be applied to optimization problems in any domain

Mechanism and Behaviour Co-optimisation of High Performance Mobile Robots

Mobile robots do not display the level of physical performance one would expect, given the specifications of their hardware. This research is based on the idea that their poor performance is at least partly due to their design, and proposes an optimisation approach for the design of high-performance mobile robots. The aim is to facilitate the design process, and produce versatile and robust robots that can exploit the maximum potential of today's technology. This can be achieved by a systematic optimisation study that is based on careful modelling of the robot's dynamics and its limitations, and takes into consideration the performance requirements that the robot is designed to meet. The approach is divided into two parts: (1) an optimisation framework, and (2) an optimisation methodology. In the framework, designs that can perform a large set of tasks are sought, by simultaneously optimising the design and the behaviours to perform them. The optimisation methodology consists of several stages, where various techniques are used for determining the design's most important parameters, and for maximising the chances of finding the best possible design based on the designer's evaluation criteria. The effectiveness of the optimisation approach is proved via a specific case-study of a high-performance balancing and hopping monopedal robot. The outcome is a robot design and a set of optimal behaviours that can meet several performance requirements of conflicting nature, by pushing the hardware to its limits in a safe way. The findings of this research demonstrate the importance of using realistic models, and taking into consideration the tasks that the robot is meant to perform in the design process

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Robot motion planning via curve shortening flows

This work will present a series of developments of geometric heat flow method in robot motion planning and estimation. The key of geometric heat flow is to formulate the motion planning problem into a curve shortening problem. By solving the geometric heat flow, an arbitrary initial curve can be deformed to a curve of minimal length, which corresponds to a feasible motion. Preliminary theories and algorithms for motion planning based on geometric heat flow have been developed for driftless control affine systems. The main contribution of this research is to extend the algorithm to robotic systems, which are dynamic systems with drifts and different types of constraint. Early stages of the research focus on adapting the algorithm to solve motion planning problems for systems with drift. To tackle systems with drift, actuated curve length and affine geometric heat flow is proposed. The method is then enriched to solve robot gymnastics motion planning, in which the effect of state constraints is encoded into curve length. Free boundary conditions are also studied to enforce the conservation of the robot's momentum. The second stage of the research focus on the construction of the geometric heat flow framework for robot locomotion planning, which involves hybrid dynamics due to contact. The activation and deactivation of phase-dependent constraints are controlled by activation functions. Lastly, to solve 3D problems in robotics, planning and estimation in SO(3) space is formulated using the geometric heat flow method