Sensitivity of Legged Balance Control to Uncertainties and Sampling Period

We propose to quantify the effect of sensor and actuator uncertainties on the control of the center of mass and center of pressure in legged robots, since this is central for maintaining their balance with a limited support polygon. Our approach is based on robust control theory, considering uncertainties that can take any value between specified bounds. This provides a principled approach to deciding optimal feedback gains. Surprisingly, our main observation is that the sampling period can be as long as 200 ms with literally no impact on maximum tracking error and, as a result, on the guarantee that balance can be maintained safely. Our findings are validated in simulations and experiments with the torquecontrolled humanoid robot Toro developed at DLR. The proposed mathematical derivations and results apply nevertheless equally to biped and quadruped robots

Sensitivity of Legged Balance Control to Uncertainties and Sampling Period

We propose to quantify the effect of sensor and actuator uncertainties on the control of the center of mass and center of pressure in legged robots, since this is central for maintaining their balance with a limited support polygon. Our approach is based on robust control theory, considering uncertainties that can take any value between specified bounds. This provides a principled approach to deciding optimal feedback gains. Surprisingly, our main observation is that the sampling period can be as long as 200 ms with literally no impact on maximum tracking error and, as a result, on the guarantee that balance can be maintained safely. Our findings are validated in simulations and experiments with the torquecontrolled humanoid robot Toro developed at DLR. The proposed mathematical derivations and results apply nevertheless equally to biped and quadruped robots

Bipedal Walking Analysis, Control, and Applications Towards Human-Like Behavior

Realizing the essentials of bipedal walking balance is one of the core studies in both robotics and biomechanics. Although the recent developments of walking control on bipedal robots have brought the humanoid automation to a different level, the walking performance is still limited compared to human walking, which also restricts the related applications in biomechanics and rehabilitation. To mitigate the discrepancy between robotic walking and human walking, this dissertation is broken into three parts to develop the control methods to improve three important perspectives: predictive walking behavior, gait optimization, and stepping strategy. To improve the predictive walking behavior captured by the model predictive control (MPC) which is transitionally applied with the nonlinear tracking control in sequence, a quadratic program (QP)-based controller is proposed to unify center of mass (COM) planning using MPC and a nonlinear torque control with control Lyapunov function (CLF). For the gait optimization, we focus on the algorithms of trajectory optimization with direct collocation framework. We propose a robust trajectory optimization using step-time sampling for a simple walker under terrain uncertainties. Towards generating human-like walking gait with multi-domain (phases), we improve the optimization through contact with more accurate transcription method for level walking, and generalize the hybrid zero dynamics (HZD) gait optimization with modified contact conditions for walking on various terrains. The results are compared with human walking gaits, where the similar trends and the sources of discrepancies are identified. In the third part for stepping strategy, we perform step estimation based on capture point (CP) for different human movements, including single-step (balance) recovery, walking and walking with slip. The analysis provides the insights of the efficacy and limitation of CP-based step estimation for human gait

Developing Empirical Predictive Models to Support Conservation Planning for Threatened Frogs, Toads, and Turtles in South-Coastal California

Amphibians and reptiles (i.e., herptiles) are among the most threatened groups of species on Earth. The major threats to these species include the direct, indirect, and synergistic effects of habitat loss and fragmentation, invasive species, disease, overexploitation, and pollution. To protect and restore species, natural resource managers need effective, data-driven conservation plans that are grounded in sound knowledge of species distributions and habitat requirements. Species distribution models (SDMs) are popular tools used to assess species-habitat relationships. However, SDMs are sensitive to the choice and quality of input data, both of which can affect model accuracy and precision and lead to erroneous conservation decisions. Although many studies have used SDMs to understand the distributions and habitats of herptiles, results are often scale dependent and cannot be generalized because of regional differences in both biotic and abiotic settings. The goal of my research was to develop and evaluate SDMs for three species of concern in south-coastal California – the Arroyo Toad, California Red-legged Frog, and Western Pond Turtle to support their conservation planning. First, I assessed if the choice of climate data sets affected the performance and interpretation of climate-based SDMs. Results indicated that SDM accuracies were affected by the choice of climate data used. Second, I developed an SDM for the amphibian pathogen, Batrochochytrium dendrobatidis (Bd) to examine the factors that likely influence its occurrence. This model identified Bd hotspots and refugia across the study area. The predictors associated with Bd occurrence included the Normalized Difference Vegetation Index (NDVI), precipitation in the wettest quarter of the year, watershed slope, annual mean temperature, and percent impervious surface. Third, I assessed how current habitat suitabilities of the three target species vary in response to climatic conditions and how they are expected to vary by midcentury (2040-2069). Results revealed that future climate change will likely reduce the availability of suitable habitats for the Arroyo Toad and Western Pond Turtle but increase available suitable habitats for the California Red-legged Frog. These findings will help inform conservation management options for the target species by identifying planning units that should be prioritized for protection

Towards Standardized Disturbance Rejection Testing of Legged Robot Locomotion with Linear Impactor: A Preliminary Study, Observations, and Implications

Dynamic locomotion in legged robots is close to industrial collaboration, but a lack of standardized testing obstructs commercialization. The issues are not merely political, theoretical, or algorithmic but also physical, indicating limited studies and comprehension regarding standard testing infrastructure and equipment. For decades, the approaches we have been testing legged robots were rarely standardizable with hand-pushing, foot-kicking, rope-dragging, stick-poking, and ball-swinging. This paper aims to bridge the gap by proposing the use of the linear impactor, a well-established tool in other standardized testing disciplines, to serve as an adaptive, repeatable, and fair disturbance rejection testing equipment for legged robots. A pneumatic linear impactor is also adopted for the case study involving the humanoid robot Digit. Three locomotion controllers are examined, including a commercial one, using a walking-in-place task against frontal impacts. The statistically best controller was able to withstand the impact momentum (26.376 kg$\cdot$m/s) on par with a reported average effective momentum from straight punches by Olympic boxers (26.506 kg$\cdot$m/s). Moreover, the case study highlights other anti-intuitive observations, demonstrations, and implications that, to the best of the authors' knowledge, are first-of-its-kind revealed in real-world testing of legged robots.Comment: A modified version of this preprint has been accepted at IEEE International Conference on Robotics and Automation (ICRA) 202

Hopping, Landing, and Balancing with Springs

This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

Torque Controlled Locomotion of a Biped Robot with Link Flexibility

When a big and heavy robot moves, it exerts large forces on the environment and on its own structure, its angular momentum can varysubstantially, and even the robot's structure can deform if there is a mechanical weakness. Under these conditions, standard locomotion controllers can fail easily. In this article, we propose a complete control scheme to work with heavy robots in torque control. The full centroidal dynamics is used to generate walking gaits online, link deflections are taken into account to estimate the robot posture and all postural instructions are designed to avoid conflicting with each other, improving balance. These choices reduce model and control errors, allowing our centroidal stabilizer to compensate for the remaining residual errors. The stabilizer and motion generator are designed together to ensure feasibility under the assumption of bounded errors. We deploy this scheme to control the locomotion of the humanoid robot Talos, whose hip links flex when walking. It allows us to reach steps of 35~cm, for an average speed of 25~cm/sec, which is among the best performances so far for torque-controlled electric robots.Comment: IEEE-RAS International Conference on Humanoid Robots (Humanoids 2022), IEEE, Nov 2022, Ginowan, Okinawa, Japa

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