Wellbore integrity is a significant problem in the U.S. and worldwide, which has serious adverse environmental and energy security consequences. Wells are constructed with a cement barrier designed to last about 50 years. Indirect measurements and models are commonly used to identify wellbore damage and leakage, often producing subjective and even erroneous results. The research presented herein focuses on new technologies to improve monitoring and detection of wellbore failures (leaks) by developing a multi-step machine learning approach to localize two types of thermal defects within a wellbore model, a prototype mechatronic system for automatically drilling small diameter holes of arbitrary depth to monitor the integrity of oil and gas wells in situ, and benchtop testing and analyses to support the development of an autonomous real-time diagnostic tool to enable sensor emplacement for monitoring wellbore integrity. Each technology was supported by experimental results. This research has provided tools to aid in the detection of wellbore leaks and significantly enhanced our understanding of the interaction between small-hole drilling and wellbore materials.
One of the greatest barriers to geothermal energy expansion is the high cost of drilling during exploration, assessment, and monitoring. Microhole drilling technology—small-diameter 2–4 in. (~5.1–10.2 cm) boreholes—is one potential low-cost alternative for monitoring and evaluating bores. However, delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes needed to realize the cost savings. Coiled tube drilling technology is one solution, but these systems are limited by the torque resistance of the coil system, helical buckling in compression, and most of all, WOB management. The evaluation presented herein will: (i) evaluate the technical and economic feasibility of low WOB technologies (specifically, a percussive hammer and a laser-mechanical system), (ii) develop downhole rotational solutions for low WOB drilling, (iii) provide specifications for a low WOB microhole drilling system, (iv) implement WOB control for low WOB drilling, and (v) evaluate and test low WOB drilling technologies.
The well documented promise of microholes has not yet matched expectations. A fundamental issue is that delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes necessary to realize the envisioned cost savings. Prior work has focused on miniaturizing the various systems used in conventional drilling technologies, such as motors, steering systems, mud handling and logging tools, and coiled tubing drilling units. As smaller diameters are targeted for these low WOB drilling technologies, several associated sets of challenges arise. For example, energy transfer efficiency in small diameter percussive hammers is different than conventional hammers. Finding adequate methods of generating rotation at the bit are also more difficult. A low weight-on-bit microhole drilling system was proposed, conceived, and tested on a limited scale. The utility of a microhole was quantified using flow analyses to establish bounds for usable microholes. Two low weight-on-bit rock reduction techniques were evaluated and developed, including a low technology readiness level concept in the laser-assisted mechanical drill and a modified commercial percussive hammer. Supporting equipment, including downhole rotation and a drill string twist reaction tool, were developed to enable wireline deployment of a drilling assembly. Although the various subsystems were tested and shown to work well individually in a laboratory environment, there is still room for improvement before the microhole drilling system is ready to be deployed. Ruggedizing the various components will be key, as well as having additional capacity in a conveyance system to provide additional capacity for pullback and deployment.
A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.
Current approaches to physical security suffer from high false alarm rates and frequent human operator involvement, despite the relative rarity of real-world threats. We present a novel architecture for autonomous adaptive physical security called autonomous detection and assessment with moving sensors (ADAMS). ADAMS is a framework for reducing nuisance and false alarms by placing mobile robotic platforms equipped with sensors outside the normal asset perimeter. These robotic agents integrate sensor data from multiple perspectives over time, and autonomously move to obtain the best new data to reduce uncertainty in the threat scene. Inferences drawn from data fused over time provide ultimate decisions regarding whether to alert human operators. This paper describes the framework and algorithms used in a prototype ADAMS implementation. We describe the results of simulations comparing this framework to alternate paradigms. These simulations show ADAMS has a 4x increase in the range at which threats are identified versus traditional static sensors, and a 5x reduction in false alarms triggered versus frameworks where all sensor detections become alarms, leading to reduced operator load. Further, these simulations show this framework for reacting to new potential threats significantly outperforms methods which merely patrol the site. We also present the results of preliminary hardware trials of an exemplar prototype system, providing limited validation of the simulations in a real-time physical demonstration.
Legged humanoid robots promise revolutionary mobility and effectiveness in environments built for humans. However, inefficient use of energy significantly limits their practical adoption. The humanoid biped walking anthropomorphic novelly-driven efficient robot for emergency response (WANDERER) achieves versatile, efficient mobility, and high endurance via novel drive-trains and passive joint mechanisms. Results of a test in which WANDERER walked for more than 4 h and covered 2.8 km on a treadmill, are presented. Results of laboratory experiments showing even more efficient walking are also presented and analyzed in this article. WANDERER's energetic performance and endurance are believed to exceed the prior literature in human-scale humanoid robots. This article describes WANDERER, the analytical methods and innovations that enable its design, and system-level energy efficiency results.
We describe the development and benchtop prototype performance characterization of a mechatronic system for automatically drilling small diameter holes of arbitrary depth, to enable monitoring the integrity of oil and gas wells in situ. The precise drilling of very small diameter, high aspect ratio holes, particularly in dimensionally constrained spaces, presents several challenges including bit buckling, limited torsional stiffness, chip clearing, and limited space for the bit and mechanism. We describe a compact mechanism that overcomes these issues by minimizing the unsupported drill bit length throughout the process, enabling the bit to be progressively fed from a chuck as depth increases. When used with flexible drill bits, holes of arbitrary depth and aspect ratio may be drilled orthogonal to the wellbore. The mechanism and a conventional drilling system are tested in deep hole drilling operation. The experimental results show that the system operates as intended and achieves holes with substantially greater aspect ratios than conventional methods with very long drill bits. The mechanism enabled successful drilling of a 1/16" diameter hole to a depth of 9", a ratio of 144:1. Dysfunctions prevented drilling of the same hole using conventional methods.
Legged robots promise radical mobility for challenging environments, but must be made more energy efficient to be practical. Historically, legged robot design has required efficiency to be traded against versatility. Much energy is lost in actuators and transmissions because few actuation systems are capable of operating efficiently across the wide range of operating conditions (e.g. different joint speeds and torques) required for legged locomotion. We describe a drivetrain topology that overcomes many of these limitations. Our approach combines high-torque electromagnetic motors and low-loss transmissions with a tailored and adjustable set of joint-specific passive mechanisms called support elements, which modulate the energy flow between motors and joints to minimize the electrical energy consumed. We present an optimization-based design method that draws on available bipedal gait data to select optimal support element configurations and parameters. Simple adjustments may be made to support elements at certain joints to enable a wide variety of locomotion with high efficiency. We present results, specific to the 3D humanoid bipedal STEPPR robot, in which support elements are co-optimized across a library of several gaits, converging on a set of designs that predict an average reduction of electrical energy of more than 50% across a set of 15 gaits, with energy savings reaching as much as 85% for some gaits. Concepts were prototyped and tested on a bench testbed, validating the predicted energy savings. Support elements were implemented on STEPPR, and energy savings of more than 35% were demonstrated.
The ability to rapidly drill through diverse, layered materials can greatly enhance future mine-rescue operations, energy exploration, and underground operations. Pneumatic-percussive drilling holds great promise in this area due to its ability to penetrate very hard materials and potential for portability. Currently such systems require expert operators who require extensive training. We envision future applications where first responders who lack such training can still respond rapidly and safely perform operations. Automated techniques can reduce the dependence on expert operators while increasing efficiency and safety. However, current progress in this area is restricted by the difficulty controlling such systems and the complexity of modeling percussive rock-bit interactions. In this work we develop and experimentally validate a novel intelligent percussive drilling architecture that is tailored to autonomously operate in diverse, layered materials. Our approach combines low-level feedback control, machine learning-based material classification, and on-line optimization. Our experimental results demonstrate the effectiveness of this approach and illustrate the performance benefits over conventional methods.
Stress corrosion cracks (SCC) represent a major concern for the structural integrity of engineered metal structures. In hazardous or restricted-access environments, remote detection of corrosion or SCC frequently relies on visual methods; however, with standard VT-1 visual inspection techniques, probabilities of SCC detection are low. Here, we develop and evaluate an improved optical sensor for SCC in restricted access-environments by combining a robotically controlled camera/fiber-optic based probe with software-based super-resolution imaging (SRI) techniques to increase image quality and detection of SCC. SRI techniques combine multiple images taken at different viewing angles, locations, or rotations, to produce a single higher- resolution composite image. We have created and tested an imaging system and algorithms for combining optimized, controlled camera movements and super- resolution imaging, improving SCC detection probabilities, and potentially revolutionizing techniques for remote visual inspections of any type.
We discuss the multiple pursuer-based intercept of a threat unmanned aerial system (UAS) with stochastic dynamics via multiple pursuing UASs, using forward stochastic reachability and receding horizon control techniques. We formulate a stochastic model for the threat that can emulate the potentially adversarial behavior and is amenable to the existing scalable results in forward stochastic reachability literature. The optimal state for the intercept for each individual pursuer is obtained via a log-concave optimization problem, and the open-loop control paths are obtained via a convex optimization problem. With stochasticity modeled as a Gaussian process, we can approximate the optimization problem as a quadratic program, to enable real-time path planning. We also incorporate real-time sensing into the path planning by using a receding horizon controller, to improve the intercept probabilities. We validate the proposed framework via hardware experiments.
Torque feedback control and series elastic actuators are widely used to enable compact, highly-geared electric motors to provide low and controllable mechanical impedance. While these approaches provide certain benefits for control, their impact on system energy consumption is not widely understood. This paper presents a model for examining the energy consumption of drivetrains implementing various target dynamic behaviors in the presence of gear reductions and torque feedback. Analysis of this model reveals that under cyclical motions for many conditions, increasing the gear ratio results in greater energy loss. A similar model is presented for series elastic actuators and used to determine the energy consequences of various spring stiffness values. Both models enable the computation and optimization of power based on specific hardware manifestations, and illustrate how energy consumption sometimes defies conventional best-practices. Results of evaluating these two topologies as part of a drivetrain design optimization for two energy-efficient electrically driven humanoids are summarized. The model presented enables robot designers to predict the energy consequences of gearing and series elasticity for future robot designs, helping to avoid substantial energy sinks that may be inadvertently introduced if these issues are not properly analyzed.
Self-excited vibrations are a major problem for rotary drilling. They may be mitigated by introducing adjustable compliance near the bottom of the drillstring, but it is challenging to identify the appropriate stiffness, particularly in situ and with limited available data on the rapidly-changing overall system dynamics. We describe an approach to modeling and simulating self-excited vibrations in drillstrings. Our approach uses impedance and admittance port functions to represent and systematically combine subsystems, and integrates established models for drillstring vibrations and rock / bit interactions. Simulations predict that intermediate stiffnesses provide better stability than either compliant or stiff extremes, which aligns with results from earlier work. Results also indicate that at least two different mechanisms limit stability in different stiffness regimes, producing significant differences in the relationship between vibration frequency and controlled module stiffness. This suggests a potential means of developing autonomous stiffness controllers that depend only on measurements taken at the variable stiffness module, without requiring a dynamic model of the rest of the drillstring.
Drilling is a repetitive, dangerous and costly process and a strong candidate for automation. We describe a method for autonomously controlling a rotary drilling process as it transitions through multiple materials with very different dynamics. This approach classifies the drilling medium based on real-time measurements and comparison to prior drilling data, and can identify the material type, drilling region, and approximately optimal set-point based on data from as few as one operating condition. The controller uses these set-points as initial conditions, and then conducts an optimal search to maximize performance, e.g. by minimizing mechanical specific energy. The control architecture is described, and the material estimation process is detailed. The results of experiments that implement autonomous drilling through a layered concrete and granite sample are discussed.
This paper describes the design and performance of a synthetic rope on sheave drive system. This system uses synthetic ropes instead of steel cables to achieve low weight and a compact form factor. We demonstrate how this system is capable of 28-Hz torque control bandwidth, 95% efficiency, and quiet operation, making it ideal for use on legged robots and other dynamic physically interactive systems. Component geometry and tailored maintenance procedures are used to achieve high endurance. Endurance tests based on walking data predict that the ropes will survive roughly 247,000 cycles when used on large (90 kg), fully actuated bipedal robot systems. The drive systems have been incorporated into two novel bipedal robots capable of three-dimensional unsupported walking. Robot data illustrate effective torque tracking and nearly silent operation. Finally, comparisons with alternative transmission designs illustrate the size, weight, and endurance advantages of using this type of synthetic rope drive system.
This paper describes how parallel elastic elements can be used to reduce energy consumption in the electric-motor-driven, fully actuated, Sandia Transmission-Efficient Prototype Promoting Research (STEPPR) bipedal walking robot without compromising or significantly limiting locomotive behaviors. A physically motivated approach is used to illustrate how selectively engaging springs for hip adduction and ankle flexion predict benefits for three different flat-ground walking gaits: human walking, human-like robot walking, and crouched robot walking. Based on locomotion data, springs are designed and substantial reductions in power consumption are demonstrated using a bench dynamometer. These lessons are then applied to STEPPR, a fully actuated bipedal robot designed to explore the impact of tailored joint mechanisms on walking efficiency. Featuring high-Torque brushless DC motors, efficient low-ratio transmissions, and high-fidelity torque control, STEPPR provides the ability to incorporate novel joint-level mechanisms without dramatically altering high-level control. Unique parallel elastic designs are incorporated into STEPPR, and walking data show that hip adduction and ankle flexion springs significantly reduce the required actuator energy at those joints for several gaits. These results suggest that parallel joint springs offer a promising means of supporting quasi-static joint torques due to body mass during walking, relieving motors of the need to support these torques and substantially improving locomotive energy efficiency.
As unmanned systems (UMS) proliferate for security and defense applications, autonomous control system capabilities that enable them to perform tactical operations are of increasing interest. These operations, in which UMS must match or exceed the performance and speed of people or manned assets, even in the presence of dynamic mission objectives and unpredictable adversary behavior, are well beyond the capability of even the most advanced control systems demonstrated to date. In this paper we deconstruct the tactical autonomy problem, identify the key technical challenges, and place them into context with the autonomy taxonomy produced by the US Department of Defense's Autonomy Community of Interest. We argue that two key capabilities beyond the state of the art are required to enable an initial fieldable capability: rapid abstract perception in appropriate environments, and tactical reasoning. We summarize our work to date in tactical reasoning, and present initial results from a new research program focused on abstract perception in tactical environments. This approach seeks to apply semantic labels to a broad set of objects via three core thrusts. First, we use physics-based multi-sensor fusion to enable generalization from imperfect and limited training data. Second, we pursue methods to optimize sensor perspective to improve object segmentation, mapping and, ultimately, classification. Finally, we assess the potential impact of using sensors that have not traditionally been used by UMS to perceive their environment, for example hyperspectral imagers, on the ability to identify objects. Our technical approach and initial results are presented.
Electric motors are a popular choice for mobile robots because they can provide high peak efficiencies, high speeds, and quiet operation. However, the continuous torque performance of these actuators is thermally limited due to joule heating, which can ultimately cause insulation breakdown. In this work we illustrate how motor housing design and active cooling can be used to significantly improve the ability of the motor to transfer heat to the environment. This can increase continuous torque density and reduce energy consumption. We present a novel housing design for brushless DC motors that provides improved heat transfer. This design achieves a 50% increase in heat transfer over a nominal design. Additionally, forced air or water cooling can be easily added to this configuration. Forced convection increases heat transfer over the nominal design by 79%with forced air and 107% with pumped water. Finally, we show how increased heat transfer reduces power consumption and we demonstrate that strategically spending energy on cooling can provide net energy savings of 4%-6%.
The dynamic stability of deep drillstrings is challenged by an inability to impart controllability with ever-changing conditions introduced by geology, depth, structural dynamic properties and operating conditions. A multi-organizational LDRD project team at Sandia National Laboratories successfully demonstrated advanced technologies for mitigating drillstring vibrations to improve the reliability of drilling systems used for construction of deep, high-value wells. Using computational modeling and dynamic substructuring techniques, the benefit of controllable actuators at discrete locations in the drillstring is determined. Prototype downhole tools were developed and evaluated in laboratory test fixtures simulating the structural dynamic response of a deep drillstring. A laboratory-based drilling applicability demonstration was conducted to demonstrate the benefit available from deployment of an autonomous, downhole tool with self-actuation capabilities in response to the dynamic response of the host drillstring. A concept is presented for a prototype drilling tool based upon the technical advances. The technology described herein is the subject of U.S. Patent Application No. 62219481, entitled "DRILLING SYSTEM VIBRATION SUPPRESSION SYSTEMS AND METHODS", filed September 16, 2015.
In this paper we introduce STEPPR (Sandia Transmission-Efficient Prototype Promoting Research), a bipedal robot designed to explore efficient bipedal walking. The initial iteration of this robot achieves efficient motions through powerful electromagnetic actuators and highly back-drivable synthetic rope transmissions. We show how the addition of parallel elastic elements at select joints is predicted to provide substantial energetic benefits: reducing cost of transport by 30 to 50 percent. Two joints in particular, hip roll and ankle pitch, reduce dissipated power over three very different gait types: human walking, human-like robot walking, and crouched robot walking. Joint springs based on this analysis are tested and validated experimentally. Finally, this paper concludes with the design of two unique parallel spring mechanisms to be added to the current STEPPR robot in order to provide improved locomotive efficiency.
Sandia’s Intelligent Systems, Robotics, and Cybernetics group (ISRC) created the Sandia Architecture for Heterogeneous Unmanned System Control (SAHUC) to demonstrate how heterogeneous multi-agent teams could be used for tactical operations including the protection of high-consequence sites. Advances in multi-agent autonomy and unmanned systems have provided revolutionary new capabilities that can be leveraged for physical security applications. SAHUC applies these capabilities to produce a command-intent driven, autonomously adapting, multi-agent mobile sensor network. This network could enhance the security of high-consequence sites; it can be quickly and intuitively re-tasked to rapidly adapt to changing security conditions. The SAHUC architecture, GUI, autonomy layers, and implementation are explored. Results from experiments and a demonstration are also discussed.
The Sandia Architecture for Heterogeneous Unmanned System Control (SAHUC) was produced as part of a three year internally funded project performed by Sandia's Intelligent Systems, Robotics, and Cybernetics group (ISRC). ISRC created SAHUC to demonstrate how teams of Unmanned Systems (UMS) can be used for small-unit tactical operations incorporated into the protection of high-consequence sites. Advances in Unmanned Systems have provided crucial autonomy capabilities that can be leveraged and adapted to physical security applications. SAHUC applies these capabilities to provide a distributed ISR network for site security. This network can be rapidly re-tasked to respond to changing security conditions. The SAHUC architecture contains multiple levels of control. At the highest level a human operator inputs objectives for the network to accomplish. The heterogeneous unmanned systems automatically decide which agents can perform which objectives and then decide the best global assignment. The assignment algorithm is based upon coarse metrics that can be produced quickly. Responsiveness was deemed more crucial than optimality for responding to time-critical physical security threats. Lower levels of control take the assigned objective, perform online path planning, execute the desired plan, and stream data (LIDAR, video, GPS) back for display on the user interface. SAHUC also retains an override capability, allowing the human operator to modify all autonomous decisions whenever necessary. SAHUC has been implemented and tested with UAVs, UGVs, and GPS-tagged blue/red force actors. The final demonstration illustrated how a small fleet, commanded by a remote human operator, could aid in securing a facility and responding to an intruder.
Our overall intent is to develop improved prosthetic devices with the use of nerve interfaces through which transected nerves may grow, such that small groups of nerve fibers come into close contact with electrode sites, each of which is connected to electronics external to the interface. These interfaces must be physically structured to allow nerve fibers to grow through them, either by being porous or by including specific channels for the axons. They must be mechanically compatible with nerves such that they promote growth and do not harm the nervous system, and biocompatible to promote nerve fiber growth and to allow close integration with biological tissue. They must exhibit selective and structured conductivity to allow the connection of electrode sites with external circuitry, and electrical properties must be tuned to enable the transmission of neural signals. Finally, the interfaces must be capable of being physically connected to external circuitry, e.g. through attached wires. We have utilized electrospinning as a tool to create conductive, porous networks of non-woven biocompatible fibers in order to meet the materials requirements for the neural interface. The biocompatible fibers were based on the known biocompatible material poly(dimethyl siloxane) (PDMS) as well as a newer biomaterial developed in our laboratories, poly(butylene fumarate) (PBF). Both of the polymers cannot be electrospun using conventional electrospinning techniques due to their low glass transition temperatures, so in situ crosslinking methodologies were developed to facilitate micro- and nano-fiber formation during electrospinning. The conductivity of the electrospun fiber mats was controlled by controlling the loading with multi-walled carbon nanotubes (MWNTs). Fabrication, electrical and materials characterization will be discussed along with initial in vivo experimental results.
Sandia's scientific and engineering expertise in the fields of computational biology, high-performance prosthetic limbs, biodetection, and bioinformatics has been applied to specific problems at the forefront of cancer research. Molecular modeling was employed to design stable mutations of the enzyme L-asparaginase with improved selectivity for asparagine over other amino acids with the potential for improved cancer chemotherapy. New electrospun polymer composites with improved electrical conductivity and mechanical compliance have been demonstrated with the promise of direct interfacing between the peripheral nervous system and the control electronics of advanced prosthetics. The capture of rare circulating tumor cells has been demonstrated on a microfluidic chip produced with a versatile fabrication processes capable of integration with existing lab-on-a-chip and biosensor technology. And software tools have been developed to increase the calculation speed of clustered heat maps for the display of relationships in large arrays of protein data. All these projects were carried out in collaboration with researchers at the University of Texas M. D. Anderson Cancer Center in Houston, TX.
Low Temperature Cofired Ceramic (LTCC) has proven to be an enabling medium for microsystem technologies, because of its desirable electrical, physical, and chemical properties coupled with its capability for rapid prototyping and scalable manufacturing of components. LTCC is viewed as an extension of hybrid microcircuits, and in that function it enables development, testing, and deployment of silicon microsystems. However, its versatility has allowed it to succeed as a microsystem medium in its own right, with applications in non-microelectronic meso-scale devices and in a range of sensor devices. Applications include silicon microfluidic ''chip-and-wire'' systems and fluid grid array (FGA)/microfluidic multichip modules using embedded channels in LTCC, and cofired electro-mechanical systems with moving parts. Both the microfluidic and mechanical system applications are enabled by sacrificial volume materials (SVM), which serve to create and maintain cavities and separation gaps during the lamination and cofiring process. SVMs consisting of thermally fugitive or partially inert materials are easily incorporated. Recognizing the premium on devices that are cofired rather than assembled, we report on functional-as-released and functional-as-fired moving parts. Additional applications for cofired transparent windows, some as small as an optical fiber, are also described. The applications described help pave the way for widespread application of LTCC to biomedical, control, analysis, characterization, and radio frequency (RF) functions for macro-meso-microsystems.
Robots for high-force interaction with humans face particular challenges to achieve performance and coupled stability. Because available actuators are unable to provide sufficiently high force density and low impedance, controllers for such machines often attempt to mask the robots physical dynamics, though this threatens stability. Controlling for passivity, the state-of-the-art means of ensuring coupled stability, inherently limits performance to levels that are often unacceptable. A controller that imposes passivity is compared to a controller designed by a new method that uses limited knowledge of human dynamics to improve performance. Both controllers were implemented on a testbed, and coupled stability and performance were tested. Results show that the new controller can improve both stability and performance. The different structures of the controllers yield key differences in physical behavior, and guidelines are provided to assist in choosing the appropriate approach for specific applications.
This report contains the results of a research effort on advanced robot locomotion. The majority of this work focuses on walking robots. Walking robot applications include delivery of special payloads to unique locations that require human locomotion to exo-skeleton human assistance applications. A walking robot could step over obstacles and move through narrow openings that a wheeled or tracked vehicle could not overcome. It could pick up and manipulate objects in ways that a standard robot gripper could not. Most importantly, a walking robot would be able to rapidly perform these tasks through an intuitive user interface that mimics natural human motion. The largest obstacle arises in emulating stability and balance control naturally present in humans but needed for bipedal locomotion in a robot. A tracked robot is bulky and limited, but a wide wheel base assures passive stability. Human bipedal motion is so common that it is taken for granted, but bipedal motion requires active balance and stability control for which the analysis is non-trivial. This report contains an extensive literature study on the state-of-the-art of legged robotics, and it additionally provides the analysis, simulation, and hardware verification of two variants of a proto-type leg design.