Conference Paper

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

Electr. Eng. & Comput. Sci. Dept., Case Western Reserve Univ., Cleveland, OH, USA
DOI: 10.1109/IROS.2010.5650200 Conference: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Source: IEEE Xplore

ABSTRACT 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.

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    ABSTRACT: 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.
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    ABSTRACT: 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 animal's 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.
    2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011; 09/2011
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    ABSTRACT: This paper addresses an improvement in navigability of a walking robot through the development of a simple method for measuring and correcting tracking errors while traversing uneven terrains. The proposed motion planning method utilizes a modular sensory system to measure the drift experienced by individual footholds in terms of translational and rotational drift errors. The method then computes the required corrections to prevent or reduce drift propagation resulting from the terrain irregularities by influencing the gait; generation scheme. Landmark navigation approach is implemented here using a ripple gait to evaluate the locomotion of the robot in terms of tracking errors. Dynamic simulations are conducted to evaluate the success of the controller while real world experiments show significant improvement in the robot's navigability for path following missions in uneven terrains.
    01/2012; 27(1). DOI:10.2316/Journal.206.2012.1.206-3589

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