Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task). In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms and control strategies to achieve adaptive interlimb coordination and the design principle for the control system of legged robots.

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Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters.

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Programmable coupled oscillators for synchronized locomotion.

Insects exhibit adaptive and versatile locomotion despite their minimal neural computing. Such locomotor patterns are generated via coordination between leg movements, i.e. an interlimb coordination, which is largely controlled in a distributed manner by neural circuits located in thoracic ganglia. However, the mechanism responsible for the interlimb coordination still remains elusive. Understanding this mechanism will help us to elucidate the fundamental control principle of animals' agile locomotion and to realize robots with legs that are truly adaptive and could not be developed solely by conventional control theories. This study aims at providing a "minimal" model of the interlimb coordination mechanism underlying hexapedal locomotion, in the hope that a single control principle could satisfactorily reproduce various aspects of insect locomotion. To this end, we introduce a novel concept we named "Tegotae", a Japanese concept describing the extent to which a perceived reaction matches an expectation. By using the Tegotae-based approach, we show that a surprisingly systematic design of local sensory feedback mechanisms essential for the interlimb coordination can be realized. We also use a hexapod robot we developed to show that our mathematical model of the interlimb coordination mechanism satisfactorily reproduces various insects' gait patterns.

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A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.

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Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots.

In this letter, we present an online learning mechanism called the dual integral learner for fast frequency adaptation in neural central pattern generator (CPG) based locomotion control of a hexapod robot. The mechanism works by modulating the CPG frequency through synaptic plasticity of the neural CPG network. The modulation is based on tracking error feedback between the CPG output and joint angle sensory feedback of the hexapod robot. As a result, the mechanism will always try to match the CPG frequency to the walking performance of the robot, thereby ensuring that the entire generated trajectory can be followed with low tracking error. Real robot experiments show that our mechanism can automatically generate a proper walking frequency for energy-efficient locomotion with respect to the robot body as well as being able to quickly adapt the frequency online within a few seconds to deal with external perturbations such as leg blocking and a variation in electrical power. These important features will allow a hexapod robot to be more robust and also extend its operating time. Finally, the generality of the mechanism is shown by successfully applying it to a compliant robotic manipulator arm called GummiArm.

https://findresearcher.sdu.dk:8443/ws/files/159311989/iROS_final.pdf

A Fast Online Frequency Adaptation Mechanism for CPG-Based Robot Motion Control

Walking animals show versatile locomotion. They can also adapt their movement according to the changes of their morphology and the environmental conditions. These emergent properties are realized by biomechanics, distributed central pattern generators (CPGs), local sensory feedback, and their interactions during body and leg movements through the environment. Based on this concept, we present here an artificial bio-inspired walking system. Its intralimb coordination is formed by multiple decoupled CPGs while its interlimb coordination is attained by the interactions between body dynamics and the environment through local sensory feedback of each leg. Simulation results show that this bio-inspired approach generates self-organizing emergent locomotion allowing the robot to adaptively form regular patterns, to stably walk while pushing an object with its front legs or performing multiple stepping of the front legs, to deal with morphological change, and to synchronize its movement with another robot during a collaborative task.

Subhi Shaker Barikhan

Papers

Multiple Decoupled CPGs with Local Sensory Feedback for Adaptive Locomotion Behaviors of Bio-inspired Walking Robots