Biologically – Plausible Load Feedback from Dynamically Scaled Robotic Model Insect Legs

Researchers have been studying the mechanisms underlying animal motor control for many years using computational models and biomimetic robots. Since testing some theories in animals can be challenging, this approach can enable unique contributions to the field. An example of a system that benefits from this modeling and robotics approach is the campaniform sensillum (CS), a kind of sensory organ used to detect the loads exerted on an insect\u27s legs. The CS on the leg are found in groups on high-stress areas of the exoskeleton and have a major influence on the adaptation of walking behavior. The challenge for studying these sensors is recording CS output from freely walking insects, which would show what the sensors detect during behavior. To address this difficulty, 3 dynamically scaled robotic models of the middle leg of the stick insect Carausius morosus (C. morosus) and the fly Drosophila melanogaster (D. melanogaster) were constructed. Two of the robotic legs model the C. morosus and are scaled to a stick insect at a ratio of 15:1 and 25:1. The robotic fly leg is scaled 400:1 to the leg of the D. melanogaster. Strain gauges are affixed to locations and orientations that are analogous to those of major CS groups. The legs were attached to a linear guide to simulate weight and they stepped on a treadmill to mimic walking. Using these robotic models, it is possible to shed light on how the nervous system of insects detects load feedback, examine the effect of different tarsi designs on load feedback, and compare the CS measurement capabilities of different insects. As mentioned earlier, robotic legs allow for any experiment to be conducted, and strain data can still be recorded, unlike animals. I subjected the 15:1 stick leg to a range of stepping conditions, including various static loading, transient loading, and leg slipping. I then processed the strain data through a previously published dynamic computational model of CS discharge. This demonstrated that the CS signal can robustly signal increasing forces at the beginning of the stance phase and decreasing forces at the end of the stance phase or when the foot slips. The same model leg can then be further expanded upon, allowing us to test how different tarsus designs affect load feedback. To isolate various morphological effects, these tarsi were developed with differing degrees of compliance, passive grip, and biomimetic structure. These experiments demonstrated that the tarsus plays a distinct role in loading the leg because of the various effects each design had on the strain. In the final experiment, two morphologically distinct insects with homologous CS groups were compared. The 400:1 robotic fly middle leg and the 25:1 robotic stick insect middle leg were used for these tests. The measured strains were notably influenced by the leg morphology, stepping kinematics, and sensor locations. Additionally, the sensor locations were lacking in one species in comparison to the other measured strains that were already being measured by the present sensors. These findings contributed to the understanding of load sensing in animal locomotion, effects of tarsal morphology, and sensory organ morphology in motor control

Spiking Neural Network that Maps from Generalized Coordinates to Cartesian Coordinates

In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used to tune the conductances between the compartments (Szczecinski et al., 2017). Also, the Product of Exponentials (POE) was used to calculate the spatial kinematic chain of the stick insect leg (Murray et al., 1994). The system accurately encodes the foot position and depends on the width of the sensory encoding curves, or the “bell curves”. Discussion of limitations and other studies that relate to this work, as well as motivation for future work are included

Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping

Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing “body” weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain

Aeacus: The Design and Realization of an Ant-Like Robotic Platform

A large volume of recyclable material is inescapably placed in landfills, despite modern recycling efforts. In order to create an effective way of recovering such material, we have designed and manufactured an ant-like robot with the potential to do so. The robot is equipped with the capacity to navigate through the uneven terrain of a trash heap with the ability to lift objects greater than its own weight. The robot is also designed with the intent of becoming part of a swarm to more effectively work over a large area, mimicking an ant colony

Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda

Die vorliegende Dissertation mit dem Titel “Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda” untersucht in drei Studien exemplarisch, wie (i) Wüstenameisen ihre Beine einsetzen um An- und Abstiege zu überwinden, wie (ii) Wüsten- und Waldameisen ein Umkippen an steilen Anstiegen vermeiden, und wie sich (iii) Madagaskar-Fauchschaben, Amerikanische Großschaben und Blaberus discoidalis Audinet-Servill, 1839 aus Rückenlagen drehen und aufrichten. Neuartige biomechanischen Beschreibungen umfassen unter anderem: Impuls- und Kraftwirkungen einzelner Ameisenbeine auf den Untergrund beim Bergauf- und Bergabklettern, Kippmomente bei kletternden Ameisen, Energiegebirge-Modelle (energy landscapes) zur Quantifizierung der Körperform für die funktionelle Beschreibung des Umdrehens aus der Rückenlage

Integrative Biomimetics of Autonomous Hexapedal Locomotion

Dürr V, Arena PP, Cruse H, et al. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Frontiers in Neurorobotics. 2019;13: 88.Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size

Identifying Important Sensory Feedback for Learning Locomotion Skills

Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there has been a lack of quantitative analysis to date. Here, we present a systematic saliency analysis that quantitatively evaluates the relative importance of different feedback states for motor skills learned through DRL. Our approach can identify the most essential feedback states for locomotion skills, including balance recovery, trotting, bounding, pacing and galloping. By using only key states including joint positions, gravity vector, base linear and angular velocities, we demonstrate that a simulated quadruped robot can achieve robust performance in various test scenarios across these distinct skills. The benchmarks using task performance metrics show that locomotion skills learned with key states can achieve comparable performance to those with all states, and the task performance or learning success rate will drop significantly if key states are missing. This work provides quantitative insights into the relationship between state observations and specific types of motor skills, serving as a guideline for robot motor learning. The proposed method is applicable to differentiable state-action mapping, such as neural network based control policies, enabling the learning of a wide range of motor skills with minimal sensing dependencies

The role of the femoral chordotonal organ in motor control, interleg coordination, and leg kinematics in Drosophila melanogaster

Legged locomotion in terrestrial animals is often essential for mating and survival, and locomotor behavior must be robust and adaptable in order to be successful. The behavioral plasticity demonstrated by animals’ ability to locomote across diverse types of terrains and to change their locomotion in a task-dependent manner highlights the flexible and modular nature of locomotor networks. The six legs of insects are under the multi-level control of local networks for each limb and limb joint in addition to over-arching central control of the local networks. These networks, consisting of pattern-generating groups of interneurons, motor neurons, and muscles, receive modifying and reinforcing feedback from sensory structures that encode motor output. Proprioceptors in the limbs monitoring their position and movement provide information to these networks that is essential for the adaptability and robustness of locomotor behavior. In insects, proprioceptors are highly diverse, and the exact role of each type in motor control has yet to be determined. Chordotonal organs, analogous to vertebrate muscle spindles, are proprioceptive stretch receptors that span joints and encode specific parameters of relative movement between body segments. In insects, when leg chordotonal organs are disabled or manipulated, interleg coordination and walking are affected, but the simple behavior of straight walking on a flat surface can still be performed. The femoral chordotonal organ (fCO) is the largest leg proprioceptor and monitors the position and movements of the tibia relative to the femur. It has long been studied for its importance in locomotor and postural control. In Drosophila melanogaster, an ideal model organism due its genetic tractability, investigations into the composition, connectivity, and function of the fCO are still in their infancy. The fCO in Drosophila contains anatomical subgroups, and the neurons within a subgroup demonstrate similar responses to movements about the femur-tibia joint. Collectively, the experiments laid out in this dissertation provide a multi-faceted analysis of the anatomy, connectivity, and functional importance of subgroups of fCO neurons in D. melanogaster. The dissertation is divided into four chapters, representing different aspects of this complex and intriguing system. First, I present a detailed analysis of the composition of the fCO and its connectivity within the peripheral and central nervous systems. I demonstrate that the fCO is made up of anatomically distinct groups of neurons, each with their own unique features in the legs and ventral nerve cord. Second, I investigated the neuropeptide profile of the fCO and demonstrate that some fCO neurons express a susbtance that is known to act as a neuromodulator. Third, I demonstrate the sufficiency of subsets of fCO neurons to elicit reflex responses, highlighting the role of the Drosophila fCO in postural control. Lastly, I take this a step further and look into the functional necessity of these neuronal subsets for intra- and interleg coordination during walking. The importance of the fCO in motor control in D. melanogaster has been considered rather minor, though research into the topic is very limited. In the work laid out herein, I highlight the complexity of the Drosophila fCO and its role in the determination of locomotor behavior