Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Gait Generation for Damaged Hexapods using Genetic Algorithm

This paper discusses the design and implementation of a Genetic Algorithm for the generation of gaits compensating for system damage on the joint level of a hexapod system. The hexapod base used for this algorithm consists of six three degree of freedom legs on a rectangular body. The purpose of this algorithm is to generate a gait such that when N motors become inoperable, as detected by the robot’s internal software, the system is able to continue moving about its environment. While algorithms like this have been implemented before, the generated gaits are a sequence of discrete foot positions. This work aims to generate continuous motions profiles for each joint of the leg rather than discrete foot positions. Previous works commonly disable an entire leg when damage occurs, instead this work aims to disable only individual joint motors

Versatile Locomotion Control of a Hexapod Robot Using a Hierarchical Network of Nonlinear Oscillator Circuits

A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central pattern generator (CPG), producing the global leg coordination pattern, coupled with six local pattern generators, each devoted to generating the trajectory of one leg. Every node comprises a simple nonlinear oscillator and is well-suited for implementation in a standard field-programmable analog array device. The network enables versatile locomotion control based on five high-level parameters which determine the inter-oscillator coupling pattern via simple rules. The controller was realized on dedicated hardware, deployed to control an ant-like hexapod robot, and multi-sensory telemetry was performed. As a function of a single parameter, it was able to stably reproduce the canonical gaits observed in six-legged insects, namely the wave, tetrapod, and tripod gaits. A second parameter enabled driving the robot in ant-like and cockroach-like postures. Three further parameters enabled inhibiting and resuming walking, steering, and producing uncoordinated movement. Emergent phenomena were observed in the form of a multitude of intermediate gaits and of hysteresis and metastability close to a point of gait transition. The primary contributions of this paper reside in the hierarchical controller architecture and associated approach for collapsing a large set of low-level parameters, stemming from the complex hexapod kinematics, into only five high-level parameters. Such parameters can be changed dynamically, an aspect of broad practical relevance opening new avenues for driving hexapod robots via afferent signals from other circuits representing higher brain areas, or by means of suitable brain-computer interfaces. An additional contribution is the detailed characterization via telemetry of the physical robot, involving the definition of parameters which may aid future comparison with other controllers. The present results renew interest into analog CPG architectures and reinforce the generality of the connectionist approach