Insect-Inspired Robots: Bridging Biological and Artificial Systems

Abstract

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

Keywords: biomimetism; biomimicry; bionics; biorobotics; hexapod; legged robotics.

Figure 1

Overview of three main components underlying complex locomotion and cognition: biomechanics, locomotion control, and high-level cognitive control. Left (right) shows examples of the three components in insects (robots). (a) Left (right) is the insect central brain (high-level neural cognitive control model). (b) Left (right) is the insect thoracic ganglia (modified from [6]) (robot locomotion control model). (c) Left (right) is a biomechanical insect (robot) leg.

Figure 2

A standard hexapod robot 3-DOF leg, based the Cataglyphis fortis ant scale 1:30. Angle $$\alpha$$ corresponds to the thorax-coxa joint position, angle $$\beta$$ corresponds to coxa-femur joint (up to now, robotic designs have often fused the trochanter-femur joint), and angle $$\gamma$$ represents femur-tibia position. Illustration: ©Camille Dégardin & Ilya Brodoline (2021).

Figure 3

Scale, segments, and joints in ant Messor barbarus. Photographic credits: Hugo Merienne, Centre de Recherches sur la Cognition Animale (CRCA UMR 5169), Toulouse, France. The relative scale of each segment (coxa, femur, tibia, tarsus) w.r.t. the coxa leg of each leg (R1, R2, R3) comes from [73]. Adapted from [82] under CC-BY License, 2019.

Figure 4

Open-source hexapod robots and educational robots available in kit form. (A) Hexy (Credits: ArcBotics). (B) Phoenix (Credits: Lynxmotion). (C) PhantomX (Credits: Interbotix Lab., Trossen Robotics). (D) T-Hex (Credits: Lynxmotion). (E) Six (Credits: EZ-Robot). (F) HEBI Robotics’ 18-DoF Daisy Hexapod, built using HEBI’s X-Series actuation hardware (Credits: ©HEBI Robotics 2021).

Figure 5

(a) Overview of locomotion control involving interlimb coordination (coordination between legs), intralimb coordination (coordination between joints in a leg), and joint compliance (a property of joint with variable stiffness). (b) Example of joint compliance generated by an adaptive bio-inspired muscle model (modified from [115]). Using this model, a leg joint can behave like dampers, struts, brakes, or springs (modified from [115]). (c) Example of intralimb coordination in the swing and stance phases of the leg and different reflexes for walking on uneven terrain (modified from [116,117,118]). (d) Example of interlimb coordination which results in different gaits (modified from [119]). The main observed gaits from slow to fast [120] include: wave gait (only one leg lifts at any given time (swing) while the remaining legs stay on the ground (stance), and the wave travels from back to front), transition gait (front and back legs of the opposite side lift together at a given time while the other legs stay on the ground), tetrapod gait (diagonal pairs of legs lift together at a given time while at least four legs stay on the ground), and tripod gait (front and back legs of one side and the middle leg of the opposite side lift together at a given time while the remaining three legs stay on the ground). While the articles specify specific gaits, it is important to note that certain insects (such as stick insects [121,122], cockroaches [123], and flies [124]) frequently change their gaits depending on their locomotion speeds and situations [125].

Figure 6

Different control approaches to robot locomotion. (a) Bio-inspired control. The upper-inset shows distributed decentralized CPGs with force feedback [158,159,160]. The middle inset shows the Walknet-control (modified from [143,161]). The lower inset shows the CPG-based control and a simplified model with six oscillators for interlimb coordination (modified from [162,163]). (b) Engineering-based control [66,164]. (c) Machine learning-based control [165,166].

Figure 7

Timeline of the development of various CPG models from 1914 to 2020.The key CPG models include a half-center oscillator in 1914 [172], the Van der Pol oscillator in 1926 [177], the Matsuoka oscillator in 1985 [178], the Matsuoka oscillator with entrainment in 1991 [179], a biophysical oscillator based on Hodgkin–Huxley neurons in 1992 [173], a connectionist oscillator based on leaky-integrator neurons in 1993 [174], a connectionist oscillator based on a cellular neural network (CNN) in 2000 [175], an SO2 neural oscillator in 2003 [176], an adaptive frequency oscillator (AFO) in 2006 [180], a neural oscillator with magnitude adaptation in 2007 [49], an adaptive chaotic oscillator in 2010 [141], an oscillator with phase resetting [181], an oscillator with continuous phase modulation [182], an oscillator with frequency adaptation through fast dynamical coupling (AFDC) in 2017 [183], a neural oscillator with fast online-error based learning in 2019 [184], and a neural oscillator with dynamical state forcing (DSF) in 2020 [185].

Figure 8

A variety of hexapod gaits with varying speeds generated by adaptive CPG-based neural locomotion control (modified from [142]). The frequency of the CPG outputs can be changed by modulating the synaptic connections of the CPG neurons with an extrinsic modulatory input $$MI$$. When the modulatory input $$MI$$ is set to 0.0, each leg steps in a wave on each side with overlap. Stepping frequency increases as $$MI$$ increases, and some legs step in pairs (see dashed enclosures). This results in insect-like gaits (Figure 5d) and various intermixed gaits. The caterpillar gait is characterized by the movement of two front, middle, or hind legs at the same time and the wave travels from back to front. Under this control approach, a transition gait as shown in Figure 5d is not found. For example, one can observe wave gaits with varying frequencies ($$MI$$ = 0.01–0.04), tetrapod gaits with varying frequencies ($$MI$$ = 0.05–0.06), caterpillar gaits with varying frequencies ($$MI$$ = 0.07–0.10), and tripod gaits with varying frequencies ($$MI$$ = 0.15–0.19). Legs are labeled as numbers 1–3 from front to back, and the left and right sides are L and R, respectively. It is worth noting that when $$MI$$ is raised above 0.17, only two different gaits comparable to tripod gait (e.g., $$MI=0.17$$) and caterpillar gait (e.g., $$MI=0.10$$) appear.

Figure 9

Block-size model of the relevant sensory modalities and neural processing centers in a Drosophila central brain. The insect’s compound eyes photo-receptors (PR) acquire visual stimuli that are initially processed in the optical lobes, and transferred to the CX, and then the neuropile responsible for visual orientation. Chemical-receptors (CR) located in the antennae are responsive to olfactory stimuli whose neural response is transferred through the antennal lobes (AL), and subsequently to the projection neurons (PN), and finally to the MBs. The olfactory and visual inputs are here integrated with the other sensory modalities although their connecting paths are still not evident. The lateral horn (LH) is an inhibitory center that is activated by the PN and the ventrolateral protocerebrum (vlpr) to affect the MBs activity. Dopaminergic (DAN) and octopaminergic neurons (OAN) provide reinforcement learning signals used by learning and memory systems. Tactile stimuli, acquired by the mechanoreceptors (MR) located in the antennae, legs and halters, are locally processed in the antennal mechanosensory and motor center (AMMC) and in the thoracic ganglia for the generation of local reflexes where the CX is also concerned.