Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain. The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed. By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Towards Agility: Definition, Benchmark and Design Considerations for Small, Quadrupedal Robots

Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity is needed for this particular aspect of locomotion. In this thesis we address and partially answer two primary questions: (Q1) What is agile legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged locomotion with a robot a reality? To answer our first question, we define agility for robot and animal alike, building a common ground for this particular component of locomotion and introduce quantitative measures to enhance robot evaluation and comparison. The definition is based on and inspired by features of agility observed in nature, sports, and suggested in robotics related publications. Using the results of this observational and literature review, we build a novel and extendable benchmark of thirteen different tasks that implement our vision of quantitatively classifying agility. All scores are calculated from simple measures, such as time, distance, angles and characteristic geometric values for robot scaling. We normalize all unit-less scores to reach comparability between different systems. An initial implementation with available robots and real agility-dogs as baseline finalize our effort of answering the first question. Bio-inspired designs introducing and benefiting from morphological aspects present in nature allowed the generation of fast, robust and energy efficient locomotion. We use engineering tools and interdisciplinary knowledge transferred from biology to build low-cost robots able to achieve a certain level of agility and as a result of this addressing our second question. This iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL, and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints. Serval presents a high level of mobility at medium speeds. With many successfully implemented skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories of real animals and replaying themotion on the robot using a mathematical interpretation), we found strengths to emphasize, weaknesses to correct and made Serval ready for future attempts to achieve even more agile locomotion. We calculated Servalâs agility scores with the result of it performing better than any of its predecessors. Our small, safe and low-cost robot is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended development. Concluding, we like to mention that Serval is able to cope with step-downs, smooth, bumpy terrain and falling orthogonally to the ground

Biolocomotion Detection in Videos

Animals locomote for various reasons: to search for food, to find suitable habitat, to pursue prey, to escape from predators, or to seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. In this dissertation, the locomotion of general biological species is referred to as biolocomotion. The goal of this dissertation is to develop a computational approach to detect biolocomotion in any unprocessed video. The ways biological entities locomote through an environment are extremely diverse. Various creatures make use of legs, wings, fins, and other means to move through the world. Significantly, the motion exhibited by the body parts to navigate through an environment can be modelled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. In this dissertation, this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) is exploited to detect biolocomotion. In particular, a computational algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic handcrafted features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison to the main proposed algorithm. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Additionally, biolocomotion annotations to an extant camouflage animals dataset also is provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity

Vision-based legged robot navigation: localisation, local planning, learning

The recent advances in legged locomotion control have made legged robots walk up staircases, go deep into underground caves, and walk in the forest. Nevertheless, autonomously achieving this task is still a challenge. Navigating and acomplishing missions in the wild relies not only on robust low-level controllers but also higher-level representations and perceptual systems that are aware of the robot's capabilities. This thesis addresses the navigation problem for legged robots. The contributions are four systems designed to exploit unique characteristics of these platforms, from the sensing setup to their advanced mobility skills over different terrain. The systems address localisation, scene understanding, and local planning, and advance the capabilities of legged robots in challenging environments. The first contribution tackles localisation with multi-camera setups available on legged platforms. It proposes a strategy to actively switch between the cameras and stay localised while operating in a visual teach and repeat context---in spite of transient changes in the environment. The second contribution focuses on local planning, effectively adding a safety layer for robot navigation. The approach uses a local map built on-the-fly to generate efficient vector field representations that enable fast and reactive navigation. The third contribution demonstrates how to improve local planning in natural environments by learning robot-specific traversability from demonstrations. The approach leverages classical and learning-based methods to enable online, onboard traversability learning. These systems are demonstrated via different robot deployments on industrial facilities, underground mines, and parklands. The thesis concludes by presenting a real-world application: an autonomous forest inventory system with legged robots. This last contribution presents a mission planning system for autonomous surveying as well as a data analysis pipeline to extract forestry attributes. The approach was experimentally validated in a field campaign in Finland, evidencing the potential that legged platforms offer for future applications in the wild

Validation of a cat activity monitor

Early detection of diseases and injuries in animals is crucial for their health and well-being. Early diagnosis can be assisted by objective registration of different types of physical activities or behaviour patterns. Monitoring specific parameters, such as changes in activity levels or habits, could serve as an indicator of underlying health issues. It can be challenging for pet owners to notice subtle changes in those characteristics at an early stage. It becomes more difficult in the case of parameters of a low frequency of occurrence, such as drinking and littering behaviours. Hydration status is extremely important in cats and changes in drinking and littering patterns could be early symptoms of potential disorders, in particular diabetes mellitus. There is a noticeable increase in owners’ awareness about the physical and mental health of their pets. With a growing demand for higher standards of tools to assess animals’ everyday habits, more technologies are being developed. Activity monitors utilizing accelerometers provide broad and continuous measures of physical activity, that enable remote and non-invasive monitoring of an individual’s actions. The aim of this study was to validate the registrations of an activity monitor. Specifically, the study aimed to assess the effectiveness of the activity monitor in detecting drinking and littering activities, which might suggest underlying health issues. To monitor these activities, this study used an activity monitor equipped with an accelerometer and attached to a collar. The validity and effectiveness of the activity monitor were established by comparing the measurements obtained from the activity collar to video recordings from the motion sensor camera. For forty-eight days, activity data on drinking and littering actions were collected from a single adult cat. Descriptive statistics were performed to summarize the main findings of the dataset to obtain key results. From the total of 5989 recordings registered by the motion sensor camera, 671 recordings containing actions of drinking and littering were selected for further analysis. Accordingly, 53 recordings were extracted from the activity monitor. This study found no correlation between the data obtained from the activity monitor and the video observations from the motion sensor camera. Further research is needed to investigate the reasons behind this lack of agreement and to improve methodologies for monitoring feline activities using activity monitors. Despite underwhelming findings, it should not rule out all potential applications in monitoring feline behaviors, managing health disorders, and promoting overall health remain promising