Humans have a complex musculoskeletal arrangement which gives them great behavioural flexibility. As well as simply moving their legs, they can modulate the impedance of them. Variable impedance has become a large field in robotics, and tailoring the impedance of a robot to a particular task can improve efficiency, stability, and potentially safety. Locomotion of a bipedal robot is a perfect example of a task for which variable impedance may provide such advantages, since it is a dynamic movement which involves periodic ground impacts. This thesis explores the creation of two novel bipedal robots with variable impedance joints. These robots aim to achieve some of the benefits of compliance, while retaining the behavioural flexibility to be truly versatile machines. The field of variable impedance actuators is explored and evaluated, before the design of the robots is presented. Of the two robots, BLUE (Bipedal Locomotion at the University of Edinburgh) has a 700mm hip rotation height, and is a saggital plane biped. miniBLUE has a hip rotation height of 465mm, and includes additional joints to allow hip adduction and abduction. Rapid prototyping techniques were utilised in the creation of both robots, and both robots are based around a custom, high performance electronics and communication architecture. The human walking cycle is analysed and a simple, parameterised representation developed. Walking trajectories gathered from human motion capture data, and generated from high level gait determinants are evaluated in dynamic simulation, and then on BLUE. With the robot being capable of locomotion, we explore the effect of varying stiffness on efficiency, and find that changing the stiffness can have an effect on the energy efficiency of the movement. Finally, we introduce a system for goal-based teleoperation of the robots, in which parameters are extracted from a user in a motion capture suit and replicated by the robot. In this way, the robot produces the same overall locomotion as the human, but with joint trajectories and stiffnesses that are more suited for its dynamics.

Legged locomotion has several interesting challenges that need to be addressed, such as the ability of dynamically walk over rough terrain like stairs or stepping stones, as well as the ability to adapt to unexpected changes in the environment and the dynamic model of the robot. This thesis is driven towards solving these challenges and makes contributions on theoretical and experimental aspects to address: dynamic walking, model uncertainty, and rough terrain. On the theoretical front, we introduce and develop a unified robust and adaptive control framework that enables the ability to enforce stability and safety-critical constraints arising from robotic motion tasks under a high level of model uncertainty. We also present a novel method of walking gait optimization and gait library to address the challenge of dynamic robotic walking over stochastically generated stepping stones with significant variations in step length and step height, and where the robot has knowledge about the location of the next discrete foothold only one step ahead. On the experimental front, our proposed methods are successfully validated on ATRIAS, an underactuated, human-scale bipedal robot. In particular, experimental demonstrations illustrate our controller being able to dynamically walk at 0.6 m/s over terrain with step length variation of 23 to 78 cm, as well as simultaneous variation in step length and step height of 35 to 60cm and -22 to 22cm respectively. In addition to that, we also successfully implemented our proposed adaptive controller on the robot, which enables the ability to carry an unknown load up to 68 lb (31 kg) while maintaining very small tracking errors of about 0.01 deg (0.0017 rad) at all joints. To be more specific, we firstly develop robust control Lyapunov function based quadratic program (CLFQP) controller and L1 adaptive control to handle model uncertainty for bipedal robots. An application is dynamic walking while carrying an unknown load. The robust CLF-QP controller can guarantee robustness via a quadratic program that can be extended further to achieve robust safety-critical control. The L1 adaptive control can estimate and adapt to the presence of model uncertainty in the system dynamics. We then present a novel methodology to achieve dynamic walking for underactuated and hybrid dynamcal bipedal robots subject to safety-critical constraints. The proposed controller is based on the combination of control Barrier functions (CBFs) and control Lyapunov functions (CLFs) implemented as a state-based online quadratic program to achieve stability under input and state constraints. The main contribution of this work is the control design to enable stable dynamical bipedal walking subject to strict safety constraints that arise due to walking over a terrain with randomly generated discrete footholds. We next introduce Exponential Control Barrier Functions (ECBFs) as means to enforce high relativedegree safety constraints for nonlinear systems. We also develop a systematic design method that enables creating the Exponential CBFs for nonlinear systems making use of tools from linear control theory. Our method creates a smooth boundary for the safety set via an exponential function, therefore is called Exponential CBFs. Similar to exponential stability and linear control, the exponential boundary of our proposed method helps to have smoother control inputs and guarantee the robustness under model uncertainty. The proposed control design is numerically validated on a relative degree 4 nonlinear system (the two-link pendulum with elastic actuators and experimentally validated on a relative degree 6 linear system (the serial cart-spring system). Thanks to these advantages of Exponential CBFs, we then can apply the method to the problem of 3D dynamic walking with varied step length and step width as well as dynamic walking on time-varying stepping stones. For the work of using CBF for stepping stones, we use only one nominal walking gait. Therefore the range of step length variation is limited ([25 : 60](cm)). In order to improve the performance, we incorporate CBF with gait library and increase the step length range significantly ([10 : 100](cm)). While handling physical constraints and step transition via CBFs appears to work well, these constraints often become active at step switching. In order to resolve this issue, we introduce the approach of 2-step periodic walking. This method not only gives better step transitions but also offers a solution for the problem of changing both step length and step height. Experimental validation on the real robot was also successful for the problem of dynamic walking on stepping stones with step lengths varied within [23 : 78](cm) and average walking speed of 0:6(m=s). In order to address the problems of robust control and safety-critical control in a unified control framework, we present a novel method of optimal robust control through a quadratic program that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems under significant model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on different applications ranging from a single-link pendulum to dynamic walking of bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to significant model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations. To solve this problem, we also present another solution of adaptive CBF-CLF controller, that enables the ability to adapt to the effect of model uncertainty to maintain both stability and safety. In comparison with the robust CBF-CLF controller, this method not only can handle a higher level of model uncertainty but is also less aggressive if there is no model uncertainty presented in the system.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24266号 / 情博第810号 / 新制||情||136(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 石井 信, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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The work seeks toward further developing a novel robotic platform capable of stable three legged locomotion. This will be accomplished by creating a robust and adaptable robotic platform capable of executing different walking strategies and taking multiple continuous steps. Previous iterations of this platform have been developed, all of which have used a single gait strategy. This study will seek to develop two new strategies. The first of which is a modification of the original strategy with theoretically improved gate robustness. A second strategy will seek to implement more advanced control techniques to create a fully stable balanced gait. / Master of Science

This dissertation explores the design, and implementation of articulated robotic tail mechanisms onboard reduced degree-of-freedom (DOF) legged robots to augment performance in terms of stability and maneuverability. Fundamentally, this research is motivated by the question of how to improve the stability and maneuverability of legged robots. The conventional approach to address these challenges is to utilize leg mechanisms that are composed of three or more active DOFs that are controlled simultaneously to provide propulsion, maneuvering, and stabilization. However, animals such as lizards and cheetahs have been observed to utilize their tails to aid in these functionalities. It is hypothesized that by using an articulated tail mechanism to aid in these functionalities onboard a legged robot, the burden on the robot's legs to simultaneously maneuver and stabilize the robot may be reduced. This could allow for simplification of the leg's design and control algorithms. In recent years, significant progress has been accomplished in the field of robotic tail implementation onboard mobile robots. However, the main limitation of this work stems from the proposed tail designs, the majority of which are composed of rigid single-body pendulums that provide a constrained workspace for center-of-mass positioning, an important characteristics for inertial adjustment applications. Inspired by lizards and cheetahs that adjust their body orientation using flexible tail motions, two novel articulated, cable driven, serpentine-like tail mechanisms are proposed. The first is the Roll-Revolute-Revolute Tail which is a 3-DOF mechanism, designed for implementation onboard a quadruped robot, that is capable of forming two mechanically decoupled tail curvatures via an s-shaped cable routing scheme and gear train system. The second is a the Discrete Modular Serpentine Tail, designed for implementation onboard a biped robot, which is a modular two-DOF mechanism that distributes motion amongst links via a multi-diameter pulley. Both tail designs utilize a cable transmission system where cables are routed about circular contoured links that maintain equal antagonistic cable displacements that can produce controlled articulated tail curvatures using a single active-DOF. Furthermore, analysis and experimental results have been presented to demonstrate the effectiveness of an articulated tail's ability to: 1) increase the manifold for center-of-mass positioning, and 2) generate enhanced inertial loading relative to conventionally implemented pendulum-like tails. In order to test the tails ability to augment the performance of legged robots, a novel Robotic Modular Leg (RML) is proposed to construct both a reduced-DOF quadrupedal and bipedal experimental platform. The RML is a modular two-DOF leg mechanism composed of two serially connected four-bar mechanisms that utilizes kinematic constraints to maintain a parallel orientation between it's flat foot and body without the use of an actuated ankle. A passive suspension system integrated into the foot enables the dissipation of impact energy and maintains a stable four point-of-contact support polygon on both flat and uneven terrain. Modeling of the combined legged robotic systems and attached articulated tails has led to the derivation of dynamic formulations that were analyzed to scale articulated tails onboard legged robots to maximize inertial adjustment capabilities resulting from tail motions and design a control scheme for tail-aided maneuvering. The tail prototypes, in conjunction with virtual simulations of the quadruped and biped robot, were used in experiments and simulations to implement and analyze the methods for maneuvering and stabilizing the proposed legged robots. Results successfully demonstrate the tails' ability to augment the performance of reduced-DOF legged robots by enabling comparable walking criteria with respect to conventional legged robots. This research provides a firm foundation for future work involving design and implementation of articulated tails onboard legged robots for enhanced inertial adjustment applications. / Ph. D.

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This thesis addresses the problem of generating smooth and efficiently executable locomotion trajectories for legged robots under contact constraints. In addition, we want the trajectories to have the property that small changes in the foot position generate small changes in the joint target path. The first part of this thesis explores methods to select poses for a legged robot that maximises the workspace reachability while maintaining stability and contact constraints. It also explores methods to select configurations based on a reduced-dimensional search of the configuration space. The second part analyses time scaling strategy which tries to minimize the execution time while obeying the velocity and acceleration constraints. These two parts effectively result in smooth feasible trajectories for legged robots. Experiments on the RoboSimian robot demonstrate the effectiveness and scalability of the strategies described for walking and climbing on a rock climbing wall.

In the context of control, the motion of a legged robot is very challenging compared with traditional fixed manipulator. Recently, many researches have been conducted to control the motion of legged robot with different techniques. On the other hand, manipulation tasks have been addressed in many applications. These researches solved either the mobility or the manipulation problems, but integrating both properties in one system is still not available. In this thesis, a control algorithm is presented to control both locomotion and manipulation in a six legged robot. Landmines detection process is considered as a case study of this project to accelerate the mine detection operation by performing both walking and scanning simultaneously. In order to qualify the robot to perform more tasks in addition to the walking task, the joint redundancy of the robot is exploited optimally. The tasks are arranged according to their importance to high level of priority and low level of priority. A new task priority redundancy resolution technique is developed to overcome the effect of the algorithmic singularities and the kinematic singularity. The computational aspects of the solution are also considered in view of a real-time implementation. Due to the dynamic changes in the size of the robot motion space, the algorithm has the ability to make a trade-off between the number of achieved tasks and the imposed constraints. Furthermore, an appropriate hierarchy is imposed in order to ensure an accurate decoupling between the executed tasks. The dynamic effect of the arm on the overall performance of the robot is attenuated by reducing the optimisation variables. The effectiveness of the method is evaluated on a Computer Aided Design (CAD) model and the simulations of the whole operation are conducted using MATLAB and SimMechanics.