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

Conference PaperinProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · November 2010with17 Reads
DOI: 10.1109/IROS.2010.5650200 · Source: IEEE Xplore
Conference: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
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.
    • "The main advantage of hexapod robots is the ability to move faster with a relatively high stability. That stability can be maintained by keeping three legs on the ground at the same time [6]. Table 1shows a sample sequence of leg movements that form the gait in our robot. "
    Article · Jan 2016
    • "While unknown terrain poses a great challenge to legged locomotion, even with full knowledge of the terrain, the robot encounters inherent slips due to loose gravel, slippery vegetation, uneven surfaces etc. Many approaches [9][6][10][4] have attempted to provide stability to the body by taking corrective actions to control the slipping joint or the leg. While individual controls can be added to each leg to prevent its slip, often the stability of the whole body is dependent on all leg positions in a coupled manner. "
    [Show abstract] [Hide abstract] ABSTRACT: Legged robots such as hexapod robots are capable of navigating in rough and unstructured terrain. When the terrain model is either known \textit{a priori} or is observed by on-board sensors, motion planners can be used to give desired motion and stability for the robot. However, unexpected leg disturbances could occur due to inaccuracies of the model or sensors or simply due to the dynamic nature of the terrain. We provide a state space based framework for stabilisation of a high dimensional multi-legged robot which detects and recovers from unexpected events such as leg slip. We experimentally evaluate our approach using a modified PhantomX hexapod robot with extended tibia segments which significantly reduces its stability. Our results show that roll and pitch stability is improved by 2x when using the proposed method.
    Full-text · Conference Paper · Jun 2014 · IEEE Transactions on Neural Networks and Learning Systems
    • "Similar to Still's CPG design, with its lack of proprioceptive feedback, it does well on a plain terrain while probably is incapable of dealing with more challenging locomotion tasks. The existence of a joint-driving CPG with coupled sensory information and adaptation has been identified in biology [26], which is simulated with modern computer techniques [27] and demonstrated in software for physical single-leg and two-legged systems [28] and in a robotic hexapod [29]. It is clear that the timing of the state change of a joint-driving CPG is controlled by both the central nervous system (CNS) commands and the position and load sensors. "
    [Show abstract] [Hide abstract] ABSTRACT: 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-${\mu}{\rm m}$ complementary metal-oxide-semiconductor process and the results are reported.
    Article · Mar 2012
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