Alexander Jacob Hunt

A biologically inspired robot for lunar In-Situ Resource Utilization

Successful long-term settlements on the Moon will need a supply of resources such as oxygen and water, yet the process of regularly transporting these resources from Earth would be prohibitively costly and dangerous. One alternative would be an approach using heterogeneous, autonomous robotic teams, which could collect and extract these resources from the surrounding environment (In-Situ Resource Utilization). The Whegs™ robotic platform, with its demonstrated capability to negotiate obstacles and traverse irregular terrain, is a good candidate for a lunar rover concept. In this research, Lunar Whegs™ is constructed as a proof-of-concept rover that would be able to navigate the surface of the moon, collect a quantity of regolith, and transport it back to a central processing station. The robot incorporates an actuated scoop, specialized feet for locomotion on loose substrates, Light Detection and Ranging (LIDAR) obstacle sensing and avoidance, and sealing and durability features for operation in an abrasive environment.

Design process and tools for dynamic neuromechanical models and robot controllers

We present a serial design process with associated tools to select parameter values for a posture and locomotion controller for simulation of a robot. The controller is constructed from dynamic neuron and synapse models and simulated with the open-source neuromechanical simulator AnimatLab 2. Each joint has a central pattern generator (CPG), whose neurons possess persistent sodium channels. The CPG rhythmically inhibits motor neurons that control the servomotor’s velocity. Sensory information coordinates the joints in the leg into a cohesive stepping motion. The parameter value design process is intended to run on a desktop computer, and has three steps. First, our tool FEEDBACKDESIGN uses classical control methods to find neural and synaptic parameter values that stably and robustly control servomotor output. This method is fast, testing over 100 parameter value variations per minute. Next, our tool CPGDESIGN generates bifurcation diagrams and phase response curves for the CPG model. This reveals neural and synaptic parameter values that produce robust oscillation cycles, whose phase can be rapidly entrained to sensory feedback. It also designs the synaptic conductance of inter-joint pathways. Finally, to understand sensitivity to parameters and how descending commands affect a leg’s stepping motion, our tool SIMSCAN runs batches of neuromechanical simulations with specified parameter values, which is useful for searching the parameter space of a complicated simulation. These design tools are demonstrated on a simulation of a robot, but may be applied to neuromechanical animal models or physical robots as well.

A biologically based neural system coordinates the joints and legs of a tetrapod.

A biologically inspired neural control system has been developed that coordinates a tetrapod trotting gait in the sagittal plane. The developed neuromechanical system is used to explore properties of connections in inter-leg and intra-leg coordination. The neural controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a planar biomechanical model of a rat with 14 joints, each actuated by a pair of antagonistic Hill muscle models. The controller generates tension in the muscles through activation of simulated motoneurons. The hind leg and inter-leg control networks are based on pathways discovered in cat research tuned to the kinematic motions of a rat. The foreleg network was developed by extrapolating analogous pathways from the hind legs. The formulated intra-leg and inter-leg networks properly coordinate the joints and produce motions similar to those of a walking rat. Changing the strength of a single inter-leg connection is sufficient to account for differences in phase timing in different trotting rats.

A rapidly reconfigurable robot for assistance in urban search and rescue

A robot is being developed for urban search and rescue missions. USAR Whegs™ implements several new features into Whegs™ robot design. It is the first quadruped Whegs™ robot of this scale. It uses differential steering and the user can rapidly change its running gear to and from tracks and wheel-legs. This is also the first implementation of carbon fiber wheel-legs on a Whegs™ vehicle. The carbon-fiber reduces the mass moment of inertia eight times compared to previous aluminum designs. The running gear can be changed in 30 seconds and the resulting connections are secure. GeoSystems Zippermast allows a camera to be deployed as high as eight feet above the robot. The robot is 47.6 cm long, can travel 1.9 meters per second on its tracks, and can climb 15 cm obstacles using its wheel-legs. A two-speed transmission is being developed to permit it to run more slowly on wheel-legs for better control on irregular terrain.

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

A dynamical model of an animal’s nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot.

Using Animal Data and Neural Dynamics to Reverse Engineer a Neuromechanical Rat Model

A baseline model for testing how afferent muscle feedback affects both timing and activation levels of muscle contractions has been constructed. We present an improved version of the neuromechanical model from our previous work [6]. This updated model has carefully tuned muscles, feedback pathways, and central pattern generators CPGs. Kinematics and force plate data from trotting rats were used to better design muscles for the legs. A recent pattern generator topology [15] is implemented to better mimic the rhythm generation and pattern formation networks in the animal. Phase-space and numerical phase response analyses reveal the dynamics underlying CPG behavior, resulting in an oscillator that produces both robust cycles and favorable perturbation responses. Training methods were used to tune synapse properties to shape desired motor neuron activation patterns. The result is a model which is capable of self-propelled hind leg stepping and will serve as a baseline as we investigate the effects changes in afferent feedback have on muscle activation patterns.

Neuromechanical Simulation of an Inter-leg Controller for Tetrapod Coordination

A biologically inspired control system has been developed for coordinating a tetrapod walking gait in the sagittal plane. The controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a simplified, planar biomechanical model of a rat with 14 joints with an antagonistic pair of Hill muscle models per joint. The controller generates tension in the muscles through activation of simulated motoneurons. Though significant portions of the controller are based on cat research, this model is capable of reproducing hind leg behavior observed in walking rats. Additionally, the applied inter-leg coordination pathways between fore and hind legs are capable of creating and maintaining coordination in this rat model. Ablation tests of the different connections involved in coordination indicate the role of each connection in providing coordination with low variability.

Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animals body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems.

A Neural Network with Central Pattern Generators Entrained by Sensory Feedback Controls Walking of a Bipedal Model

A neuromechanical simulation of a planar, bipedal walking robot has been developed. It is constructed as a simplified musculoskeletal system to mimic the biomechanics of the human lower body. The controller consists of a dynamic neural network with central pattern generators (CPGs) entrained by force and movement sensory feedback to generate appropriate muscle forces for walking. The CPG model is a two-level architecture, which consists of separate rhythm generator (RG) and pattern formation (PF) networks. The presented planar biped model walks stably in the sagittal plane without inertial sensors or a centralized posture controller or a “baby walker” to help overcome gravity. Its gait is similar to humans’ with a walking speed of 1.2 m/s. The model walks over small obstacles (5 % of the leg length) and up and down 5° slopes without any additional higher level control actions.

Novel Locomotion via Biological Inspiration

Animal behavioral, physiological and neurobiological studies are providing a wealth of inspirational data for robot design and control. Several very different biologically inspired mobile robots will be reviewed. A robot called DIGbot is being developed that moves independent of the direction of gravity using Distributed Inward Gripping (DIG) as a rapid and robust attachment mechanism observed in climbing animals. DIGbot is an 18 degree of freedom hexapod with onboard power and control systems. Passive compliance in its feet, which is inspired by the flexible tarsus of the cockroach, increases the robustness of the adhesion strategy and enables DIGbot to execute large steps and stationary turns while walking on mesh screens. A Whegs™ robot, inspired by insect locomotion principles, is being developed that can be rapidly reconfigured between tracks and wheel-legs and carry GeoSystems Zipper Mast. The mechanisms that cause it to passively change its gait on irregular terrain have been integrated into its hubs for a compact and modular design. The robot is designed to move smoothly on moderately rugged terrain using its tracks and run on irregular terrain and stairs using its wheel-legs. We are also developing soft bodied robots that use peristalsis, the same method of locomotion earthworms use. We present a technique of using a braided mesh exterior to produce fluid waves of motion along the body of the robot that increase the robots speed relative to previous designs. The concept is highly scalable, for endoscopes to water, oil or gas line inspection.