Abstract
Simulating the locomotion of insects is beneficial to many areas such as experimental biology, computer animation and robotics. This work proposes a neuro-musculo-skeletal model, which integrates the biological inspirations from real insects and reproduces the gait pattern on virtual insects. The neural system is a network of spiking neurons, whose spiking patterns are controlled by the input currents. The spiking pattern provides a uniform representation of sensory information, high-level commands and control strategy. The muscle models are designed following the characteristic Hill-type muscle with customized force-length and force-velocity relationships. The model parameters, including both the neural and muscular components, are optimized via an approach of evolutionary optimization, with the data captured from real insects. The results show that the simulated gait pattern, including joint trajectories, matches the experimental data collected from real ants walking in the free mode. The simulated character is capable of moving at different directions and traversing uneven terrains.
Highlights
Insects are the largest group of species on the planet[1]
We propose a uniform encoding of sensory information, high-level commands and control strategy, with the spiking patterns of the neural system
Previous work uses a network of nonlinear oscillator to construct the Central Pattern Generator[11]
Summary
Insects are the largest group of species on the planet[1]. Their success is critically dependent on their mobility. Simulating the locomotion of virtual insects are of great interest to researchers from various fields including experimental biology, computer animation and robotics. Previous methods[11,12] modelled the controller of virtual insects as a network of nonlinear oscillators and tuned the parameters of the network via the optimization strategy of Covariance Matrix Adaptation (CMA). Real organisms, including insects, use spiking neurons for the general purposes of environmental sensory, information transmission, decision-making and muscle actuation. Such observation inspires this work to explore the solution of using spiking neuron, as the alternative building block to nonlinear oscillator, to construct the neural system
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