William A. Lewinger

Insect-like Antennal Sensing for Climbing and Tunneling Behavior in a Biologically-inspired Mobile Robot

Through the use of mechanical, actuated antennae a biologically-inspired robot is capable of autonomous decision-making and navigation when faced with an obstacle that can be climbed over or tunneled under. Vertically-sweeping mechanical antennae and interface microcontrollers have been added to the Whegs ™ II [1] sensor platform that allow it to autonomously sense the presence of, and successfully navigate a horizontal shelf placed in its path. The obstacle is sensed when the antennae make contact with it, and navigation is made possible through articulation of the Whegs ™ II body flexion joint.

A hexapod walks over irregular terrain using a controller adapted from an insect's nervous system

Insects have long been a source of inspiration for the design and implementation of legged robots. Their extraordinary mobility, agility, and adaptability are features sought after when developing competent, useful mobile walkers. Externally witnessed behaviors have been successfully implemented in walking robots for decades with great success. More recent years of biological study have solved some of the mysteries surrounding the actual neurobiological methods for mobilizing these legged wonders. This paper describes the first implementation of these neurobiological mechanisms in a physical hexapod robot that is capable of generating adaptive stepping actions with the same underlying control method as an insect.

Obstacle Avoidance Behavior for a Biologically-inspired Mobile Robot Using Binaural Ultrasonic Sensors

Many untethered mobile robots require an operators vision and intelligence for guidance and navigation. Animals and insects, however, use sensory systems such as hearing, and tactile inputs to move autonomously through their environment. This paper discusses the implementation of a simple binaural sensory pod using an ultrasonic emitter and two receivers on a mobile robot that employs legged-style locomotion. A series of obstacle avoidance behaviors programmed onto a microcontroller allow the robot is to successfully navigate a cluttered environment both semi-autonomously and autonomously.

Insect-inspired, Actively Compliant Hexapod Capable of Object Manipulation

Insects, in general, are agile creatures capable of navigating uneven and difficult terrain with ease. The leaf-cutter ants (Atta), specifically, are agile, social insects capable of navigating uneven and difficult terrain, manipulating objects in their environment, broadcasting messages to other leaf-cutter ants, performing collective tasks, and operating in cooperative manners with others of their kind [9][12]. These traits are desirable in a mobile robot. However, no robots have been developed that encompass all of these capabilities. As such, this research developed the Biologically-Inspired Legged-Locomotion Ant prototype (BILL-ANT-p) to fill the void. This paper discusses the features, development, and implementation of the BILL-Ant-p robot, quantifies its capabilities for use as a compliant mobile platform that is capable of object manipulation.

Neuromorphic Control of Stepping Pattern Generation: A Dynamic Model With Analog Circuit Implementation

Animals such as stick insects can adaptively walk on complex terrains by dynamically adjusting their stepping motion patterns. Inspired by the coupled Matsuoka and resonate-and-fire neuron models, we present a nonlinear oscillation model as the neuromorphic central pattern generator (CPG) for rhythmic stepping pattern generation. This dynamic model can also be used to actuate the motoneurons on a leg joint with adjustable driving frequencies and duty cycles by changing a few of the model parameters while operating such that different stepping patterns can be generated. A novel mixed-signal integrated circuit design of this dynamic model is subsequently implemented, which, although simplified, shares the equivalent output performance in terms of the adjustable frequency and duty cycle. Three identical CPG models being used to drive three joints can make an arthropod leg of three degrees of freedom. With appropriate initial circuit parameter settings, and thus suitable phase lags among joints, the leg is expected to walk on a complex terrain with adaptive steps. The adaptation is associated with the circuit parameters mediated both by the higher level nervous system and the lower level sensory signals. The model is realized using a 0.3- complementary metal-oxide-semiconductor process and the results are reported.

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.

Simple Muscle Models Regularize Motion in a Robotic Leg with Neurally-Based Step Generation

Robotic control systems inspired by animals are enticing to the robot designer due to their promises of simplicity, elegance and robustness. While there has been success in applying general and behaviorally-based knowledge of biological systems to control, we are investigating the use of control based on known and hypothesized neural pathways in specific model animals. Neural motor systems in animals are only meaningful in the context of their mechanical body, and the behavior of the system can be highly dependent on nonlinear and dynamic properties of the mechanical part of the system. It is therefore reasonable to believe that to reproduce behavior, the physical characteristics of the biological system must also be modeled or accounted for. In this paper we examine the performance of a robotic system with three types of muscle model: null, piecewise-constant, and linear. Results show that adding very simple models of muscle properties at a single joint cause marked improvement in the performance of a neurally-based step generator for a 3-degree-of-freedom robotic leg.

Descending commands to an insect leg controller network cause smooth behavioral transitions

Biological inspiration has long been pursued as a key to more efficient, agile and elegant control in robotics. It has been a successful strategy in the design and control of robots with both biologically abstracted and biomimetic designs. Behavioral studies have resulted in a good understanding of the mechanics of certain animals. However, without a better understanding of their nervous systems, the biologically-inspired observation-based approach was limited. The findings of Hess and Büschges, and Ekeberg et al. describing the neural mechanisms of stick insect intra-leg joint coordination have made it possible to control models of insect legs with a network of neural pathways they found in the animals thoracic ganglia. Our work with this model, further informed by cockroach neurobiological studies performed in the Ritzmann lab, has led to LegConNet (Leg Controller Network). In this paper we show that LegConNet controls the forward stepping motion of a robotic leg. With hypothesized additional pathways, some later confirmed by neurobiology, it can smoothly transition the leg from forward stepping to turning movements. We hypothesize that commands descending from a higher center in the nervous system inhibit or excite appropriate local neural pathways and change thresholds, which, in turn, create a cascade of reflexes resulting in behavioral transitions.

A hexapod robot modeled on the stick insect, Carausius morosus

Robot builders have often used insects as a source of inspiration when designing their mechanical systems, due to their ability to easily navigate uneven terrain, overcome or avoid obstacles, and adjust gaits based on traveling speed. Robotics has borrowed from nature with varying degrees of abstraction, from physical appearance to observed behaviours. This paper describes the design and construction of a robotic hexapod based on the stick insect, Carausius morosus. Physically, it is an 18.8:1 scale representation of the insect with 3-DoF legs. The to-scale design was chosen to provide similar physical attributes, such as joint and leg locations, sizes, and ranges-of-motion, which will allow more meaningful comparisons between robot performance and actual insect movements (as opposed to arbitrary hexapod designs). A custom-designed leg control board is responsible for deciding leg joint movements based on a model of the neurobiological systems identified in the insect. A distributed network of six boards will be used to control the legs based on internal parameters that can be modulated by descending commands or adaptively altered by ascending sensory signals when interacting with the environment. Our final aim in this work is to add a vision system to create depth maps, which will be used as an input to a learning system, coupled with the mechanical sensory system, such that terrain that triggers reflex actions can be associated with visual cues in order to predictively avoid obstacles and potholes.

BILL-LEGS: Low computation emergent gait system for small mobile robots

For almost two decades, Holk Cruses leg coordination method has been used as a control basis for generating gaits in legged robots. His stick insect inspired method has been successfully implemented for a number of robots such as Robot I and Robot II, the TUM Walking Machine, Tarry II, and BILL-Ant-p. However, some engineers have had difficulties implementing the controller when trying to select robust mechanism influence weights that are immune to variations in starting pose and leg speed. Additionally, the coordination method can be overwhelming for low- computation capable microcontrollers preferred for small, untethered mobile robots. The Biologically-Inspired Legged Locomotion-Low computation Emergent Gait System (BILL-LEGS) was developed as a solution to some of these issues. This method borrows heavily from Cruses original design with some modifications that allow it to be implemented on small, autonomous legged robots using simple microcontrollers. This paper describes the BILL-LEGS method and its performance during simulation. Additionally, data are presented that show its robustness to mechanism weight selection and its generation of stable gaits, independent of leg starting positions and leg movement speeds.