Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans

HyperDog: An Open-Source Quadruped Robot Platform Based on ROS2 and micro-ROS

Nowadays, design and development of legged quadruped robots is a quite active area of scientific research. In fact, the legged robots have become popular due to their capabilities to adapt to harsh terrains and diverse environmental conditions in comparison to other mobile robots. With the higher demand for legged robot experiments, more researches and engineers need an affordable and quick way of locomotion algorithm development. In this paper, we present a new open source quadruped robot HyperDog platform, which features 12 RC servo motors, onboard NVIDIA Jetson nano computer and STM32F4 Discovery board. HyperDog is an open-source platform for quadruped robotic software development, which is based on Robot Operating System 2 (ROS2) and micro-ROS. Moreover, the HyperDog is a quadrupedal robotic dog entirely built from 3D printed parts and carbon fiber, which allows the robot to have light weight and good strength. The idea of this work is to demonstrate an affordable and customizable way of robot development and provide researches and engineers with the legged robot platform, where different algorithms can be tested and validated in simulation and real environment. The developed project with code is available on GitHub (https://github.com/NDHANA94/hyperdog_ros2).Comment: 6 pages, 13 figures, IEEE SMC 2022 conferenc

Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...

Sabertooth: A High Mobility Quadrupedal Robot Platform

Team Sabertooth aimed to design and realize an innovative high mobility, quadrupedal robot platform capable of delivering a payload over terrain otherwise impassable by wheeled vehicles at a speed of 5 feet per second. The robot uses a spring system in each of its legs for energy efficient locomotion. The 4ft x 3ft x 3ft freestanding four legged robot weighs approximately 300 pounds with an additional payload capacity of 30 pounds. An important feature of the robot is the passive, two degree of freedom body joint which allows flexibility in terms of robot motions for going around tight corners and ascending stairs. A distributed control and software architecture is used for world mapping, path planning and motion control

Sabertooth: A High Mobility Quadrupedal Robot Platform

Team Sabertooth aimed to design and realize an innovative high mobility, quadrupedal robot capable of delivering a payload over terrain impassable by wheeled vehicles at a speed of 5fps. The robot is designed to ascend and descend stairs. The robot uses a spring system in each of its legs for energy efficient locomotion. The 4\u27x3\u27x3\u27 freestanding four legged robot weighs approximately 300lbs with an additional payload capacity of 30lbs. The passive two degree of freedom body joint allows flexibility in terms of robot motion for going around tight corners and ascending stairs. The system integrates sensors for staircase recognition, obstacle avoidance, and distance calculation. A distributed control and software architecture is used for world mapping, path planning and motion control

Estimation of skeletal kinematics in freely moving rodents

Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior; however, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics using surface-tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units. Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model

Ros-based control of a robotic leg for a quadruped robot

The sector of Autonomous Mobile Robots (AMR) has grown a lot during the last years. In the literature an AMR is a robot able to move without any human operator control. With the im- provements of the control systems, robots have gained a lot of dexterity and flexibility in the movements, migrating from restrictive mechanical systems like wheeling. AMR with wheels are very efficient on plane grounds, like conventional industrial environ- ments. Nevertheless, they lose efficiency when dealing with rough terrains like the ones you can find on mountain rescue, vineyards or building industry. A good alternative is to use legged robots, which imitate animal walking behaviour, for these types of terrain since they are able to easily overcome these obstacles. The objective of this project is to create a control system for the robotic leg of a quadruped robot. A mechanical leg was developed and implemented at the CDEI for a quadruped robot, aimed for its locomotion in rugged and unknown terrain. This project will create the control system for this leg, so that it can execute the desired motions and it can be later integrated in the com- plete quadruped robot. The system will be designed so that it can be part of the stack of the quadruped robot. In this sense, the control systems software will be developed using the Robot Operating System (ROS) and MATLAB&Simulin

Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions

Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.Comment: 37 pages, 22 figures, currently under review (journal

Quadruped Pupper Robotics: Dynamics and Control

The purpose of this project is to provide insights on the Pupper Robot, from Hands-On Robotics (handsonrobotics.org), for future studies and research. The Hands-On Robotics (HOR) team aims to provide robotics kits and educational curricula to explore agile locomotion, motor control, and AI for community colleges and high schools. We worked with the HOR team in this project to help them better achieve their goals. The main objectives of this project include: 1. Build the robot and analyze the dynamical behaviors of the robot. 2. Investigate the robot control from both hardware and software perspectives. 3. Design a new gait for the Pupper Robot. 4. Create an implementation guide for future groups, documenting knowledge we have learned during the project. By the end of this project, we achieved the following: A. Built a fully functioning robot. B. Investigated the theoretical underpinnings of quadruped robots, including inverse kinematics and gait generation theories. C. Understood and reflected on the control structure of the robot. D. Implemented a new jumping gait which allows the robot to leap forward and land on balance. E. Composed detailed guides on robot building instructions, controller files installation, simulator installation, and simulator modifications

Neural and behavioral bases of innate behaviors

Recently, ethological studies of animal behavior uncovered its complexity while neuroscientific work began unraveling the neural bases of behavior. Improvements in algorithmic understanding of behavior and neural function contributed to re- cent breakthroughs in robotics and artificial intelligence systems. Yet, animals’ decision-making and motor-control are unequalled by human engineered systems and the continued investigation of the behavioral and neural bases of these abilities is crucial for understanding brain function and inform further technological devel- opments. In my PhD work, I first investigate escape path selection in mice presented with threat, demonstrating how mice combined rapidly acquired spatial knowledge with an innate choice heuristic to inform decision-making. This strategy minimizes the requirement for trial-and-error learning and yields accurate decision-making by combining knowledge acquired at an evolutionarily time-scale with that acquired by the individual. Future work aimed at understanding how these sources of in- formation are combined in the brain to inform decision-making may lead to more efficient artificial learning agents. Next, I studied goal-directed locomotion behav- ior in which mice move rapidly through an environment to reach a goal location. Successful goal-directed locomotion behavior requires substantial navigation and motor control skills and, additionally, sophisticated planning and control of move- ments while moving at high speed. Detailed behavioral quantification and compar- ison to a control-theoretic model demonstrated that mice do possess such planning skills, allowing them to execute rapid and efficient trajectories to a goal. Population- level extracellular recordings of neural activity during goal directed locomotion was also used to begin uncovering the neural bases of planning during locomotion. Altogether, my work combined accurate quantification of animal movements with the- oretical models of optimal behavior to understand behavior at a computation level, aiming to provide crucial information to inform future studies on the neural bases of innate behaviors and aid in the development of novel artificial learning system