In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

GaitKeeper: a system for measuring canine gait

It is understood gait has the potential to be used as a window into neurodegenerative disorders, identify markers of subclinical pathology, inform diagnostic algorithms of disease progression and measure the efficacy of interventions. Dogs’ gaits are frequently assessed in a veterinary setting to detect signs of lameness. Despite this, a reliable, affordable and objective method to assess lameness in dogs is lacking. Most described canine lameness assessments are subjective, unvalidated and at high risk of bias. This means reliable, early detection of canine gait abnormalities is challenging, which may have detrimental implications for dogs’ welfare. In this paper, we draw from approaches and technologies used in human movement science and describe a system for objectively measuring temporal gait characteristics in dogs (step-time, swing-time, stance-time). Asymmetries and variabilities in these characteristics are of known clinical significance when assessing lameness but presently may only be assessed on coarse scales or under highly instrumented environments. The system consists an inertial measurement unit, containing a 3-axis accelerometer and gyroscope coupled with a standardized walking course. The measurement unit is attached to each leg of the dog under assessment before it is walked around the course. The data by the measurement unit is then processed to identify steps and subsequently, micro-gait characteristics. This method has been tested on a cohort of 19 healthy dogs of various breeds ranging in height from 34.2 cm to 84.9 cm. We report the system as capable of making precise step delineations with detections of initial and final contact times of foot-to-floor to a mean precision of 0.011 s and 0.048 s, respectively. Results are based on analysis of 12,678 foot falls and we report a sensitivity, positive predictive value and F-score of 0.81, 0.83 and 0.82 respectively. To investigate the effect of gait on system performance, the approach was tested in both walking and trotting with no significant performance deviation with 7249 steps reported for a walking gait and 4977 for a trotting gait. The number of steps reported for each leg were approximately equal and this consistency was true in both walking and trotting gaits. In the walking gait 1965, 1790, 1726 and 1768 steps were reported for the front left, front right, hind left and hind right legs respectively. 1361, 1250, 1176 and 1190 steps were reported for each of the four legs in the trotting gait. The proposed system is a pragmatic and precise solution for obtaining objective measurements of canine gait. With further development, it promises potential for a wide range of applications in both research and clinical practice

Towards understanding human locomotion

Die zentrale Frage, die in der vorliegenden Arbeit untersucht wurde, ist, wie man die komplizierte Dynamik des menschlichen Laufens besser verstehen kann. In der wissenschaftlichen Literatur werden zur Beschreibung von Laufbewegungen (Gehen und Rennen) oftmals minimalistische "Template"-Modelle verwendet. Diese sehr einfachen Modelle beschreiben nur einen ausgewählten Teil der Dynamik, meistens die Schwerpunktsbahn. In dieser Arbeit wird nun versucht, mittels Template-Modellen das Verständnis des Laufens voranzubringen. Die Analyse der Schwerpunktsbewegung durch Template-Modelle setzt eine präzise Bestimmung der Schwerpunktsbahn im Experiment voraus. Hierfür wird in Kapitel 2.3 eine neue Methode vorgestellt, welche besonders robust gegen die typischen Messfehler bei Laufexperimenten ist. Die am häfigsten verwendeten Template-Modelle sind das Masse-Feder-Modell und das inverse Pendel, welche zur Beschreibung der Körperschwerpunktsbewegung gedacht sind und das Drehmoment um den Schwerpunkt vernachlässigen. Zur Beschreibung der Stabilisierung der Körperhaltung (und damit der Drehimpulsbilanz) wird in Abschnitt 3.3 das Template-Modell "virtuelles Pendel" für das menschliche Gehen eingeführt und mit experimentellen Daten verglichen. Die Diskussion möglicher Realisierungsmechanismen legt dabei nahe, dass die Aufrichtung des menschlichen Gangs im Laufe der Evolution keine große mechanische Hürde war. In der Literatur wird oft versucht, Eigenschaften der Bewegung wie Stabilität durch Eigenschaften der Template-Modelle zu erklären. Dies wird in modifizierter Form auch in der vorliegen Arbeit getan. Hierzu wird zunächst eine experimentell bestimmte Schwerpunktsbewegung auf das Masse-Feder-Modell übertragen. Anschließend wird die Kontrollvorschrift der Schritt-zu-Schritt-Anpassung der Modellparameter identifiziert sowie eine geeignete Näherung angegeben, um die Stabilität des Modells, welches mit dieser Kontrollvorschrift ausgestattet wird, zu analysieren. Der Vergleich mit einer direkten Bestimmung der Stabilität aus einem Floquet-Modell zeigt qualitativ gute Übereinstimmung. Beide Ansätze führen auf das Ergebnis, dass beim langsamen menschlichen Rennen Störungen innerhalb von zwei Schritten weitgehend abgebaut werden. Zusammenfassend wurde gezeigt, wie Template-Modelle zum Verständnis der Laufbewegung beitragen können. Gerade die Identifikation der individuellen Kontrollvorschrift auf der Abstraktionsebene des Masse-Feder-Modells erlaubt zukünftig neue Wege, aktive Prothesen oder Orthesen in menschenähnlicher Weise zu steuern und ebnet den Weg, menschliches Rennen auf Roboter zu übertragen.Human locomotion is part of our everyday life, however the mechanisms are not well enough understood to be transferred into technical devices like orthoses, protheses or humanoid robots. In biomechanics often minimalistic so-called template models are used to describe locomotion. While these abstract models in principle offer a language to describe both human behavior and technical control input, the relation between human locomotion and locomotion of these templates was long unclear. This thesis focusses on how human locomotion can be described and analyzed using template models. Often, human running is described using the SLIP template. Here, it is shown that SLIP (possibly equipped with any controller) cannot show human-like disturbance reactions, and an appropriate extension of SLIP is proposed. Further, a new template to describe postural stabilization is proposed. Summarizing, this theses shows how simple template models can be used to enhance the understanding of human gait

Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations

Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach

This paper presents a hierarchical control strategy based on hybrid systems theory, nonlinear control, and safety-critical systems to enable cooperative locomotion of robotic guide dogs and visually impaired people. We address high-dimensional and complex hybrid dynamical models that represent collaborative locomotion. At the high level of the control scheme, local and nonlinear baseline controllers, based on the virtual constraints approach, are designed to induce exponentially stable dynamic gaits. The baseline controller for the leash is assumed to be a nonlinear controller that keeps the human in a safe distance from the dog while following it. At the lower level, a real-time quadratic programming (QP) is solved for modifying the baseline controllers of the robot as well as the leash to avoid obstacles. In particular, the QP framework is set up based on control barrier functions (CBFs) to compute optimal control inputs that guarantee safety while being close to the baseline controllers. The stability of the complex periodic gaits is investigated through the Poincare return map. To demonstrate the power of the analytical foundation, the control algorithms are transferred into an extensive numerical simulation of a complex model that represents cooperative locomotion of a quadrupedal robot, referred to as Vision 60, and a human model. The complex model has 16 continuous-time domains with 60 state variables and 20 control inputs

The inertial properties of the German Shepherd

Previously held under moratorium from 30th November 2016 until 30th November 2021The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution.The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddy

Non-invasive, objective determination of pain using pressure platform gait analysis : the effect of post-operative analgesic protocol and surgical method on limb function in cats following onychectomy

Our first objective was to perform pressure platform gait analysis on 26 adult cats that had or had not undergone bilateral forelimb onychectomy to determine peak vertical force (PVF) and vertical impulse (VI). The PVF and VI were collected from all limbs of each cat with a 2-m long pressure platform walkway. No significant difference was found for PVF and VI between cats that had or had not had onychectomy. Results suggest that bilateral forelimb onychectomy did not result in altered vertical forces measured more than 6 months after surgery in cats. Our second objective was to perform pressure-platform gait analysis on 27 adult cats to document the analgesic effects of topical administration of bupivacaine, IM administration of butorphanol, and transdermal fentanyl following onychectomy. Peak vertical force (PVF) and vertical impulse (VI) data were collected before and 1, 2, 3, and 12 days after unilateral (left forelimb) onychectomy. Two days after surgery, cats treated with bupivacaine had significantly lower PVF than did cats in the other groups. Results suggest that limb function following onychectomy is significantly better in cats treated with fentanyl transdermally or butorphanol IM than in cats treated with bupivacaine topically. Regardless of the analgesic regimen, limb function was still significantly reduced 12 days after surgery, suggesting that long-term analgesic treatment should be considered for cats undergoing onychectomy. Our third objective was to compare the level of post-operative limb function and discomfort in cats after scalpel and laser onychectomy as measured by pressure platform gait analysis on 20 healthy adult cats. Peak vertical force (PVF) and vertical impulse (VI) data were collected as described for the second objective. Cats in the laser group had significantly higher ground reaction forces (GRFs) on days 1 and 2 and significantly higher PVF ratio on day 12 when compared to cats in the scalpel group. Results suggest that cats have improved limb function following onychectomy when performed with a CO₂ laser as compared to with a scalpel