Evolution strategies combined with central pattern generators for head motion minimization during quadruped robot locomotion

In autonomous robotics, the head shaking induced by locomotion is a relevant and still not solved problem. This problem constraints stable image acquisition and the possibility to rely on that information to act accordingly. In this article, we propose a movement controller to generate locomotion and head movement. Our aim is to generate the head movement required to minimize the head motion induced by locomotion itself. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs). CPGs are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. Based on these ideas, we propose a combined approach to generate head movement stabilization on a quadruped robot, using CPGs and an evolution strategy. The best set of parameters that generates the head movement are computed by an evolution strategy. Experiments were performed on a simulated AIBO robot. The obtained results demonstrate the feasibility of the approach, by reducing the overall head movement

Combining central pattern generators with the electromagnetism-like algorithm for head motion stabilization during quadruped robot locomotion

Visually-guided locomotion is important for autonomous robotics. However, there are several difficulties, for instance, the head shaking that results from the robot locomotion itself that constraints stable image acquisition and the possibility to rely on that information to act accordingly. In this article, we propose a controller architecture that is able to generate locomotion for a quadruped robot and to generate head motion able to minimize the head motion induced by locomotion itself. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs). CPGs are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. We take advantage of this particularity and propose a combined approach to generate head movement stabilization on a quadruped robot, using CPGs and a global optimization algorithm. The best set of parameters that generates the head movement are computed by the electromagnetism-like algorithm in order to reduce the head shaking caused by locomotion. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that does not eliminate but reduces the one induced by locomotion

Vertical motion control of a one legged hopping robot

Hopping movement is a desirable locomotion for a mobile robot to adapt on unknown surface and overcome the obstacles avoidance problem. The hopping locomotion is one of locomotion produced by legged robot. The legged type robot has difficult mechanism and complexity in control system. The hopping robot is designed to avoid the obstacles vertically. So, if the hopping robot takes too long time to reach the desired height, it will produced damages to the hopping robot physical. Therefore, the research on develop control strategies of one legged hopping robot is useful so that the developed control strategies can be used and extended to the multi-legged system. Central Pattern Generator (CPG) is a neural network that capable to generate continuous and rhythmic pattern. Since the hopping movement is a continuous and rhythmic jumping movement, it is synthesized that CPG neural network capable to generate hopping movement. Thus, the objectives of this research is to model the one legged hopping robot experimentally, to design a classic controller and integrate with CPG to compensate the steady-state error at each different height, and to optimize the parameters values of Central Pattern Generator (CPG) for the optimum rise time and steady-state error. A hopping peak height detector algorithm is designed to determine hopping peak height as feedback loop. The PI-CPG neural network parameters are optimized for each reference hopping height via simulation. The performance of optimized PI-CPG neural network is evaluated and compared with optimized PI and PID controller. The result shows that the optimized PI-CPG neural network controller produced better response which is 21.36 %, 24.20 %, and 44.13 % average rise time faster than PI-CPG, optimized PI, and optimized PID controller respectively. Moreover, the optimized PI-CPG controller more accurate in term of 4.91 % steady-state error compared to PI-CPG controller; 8.69 %, optimized PI controller; 6.03 %, and optimized PID controller 12.52 % average steady-state error for each reference hopping height. As a conclusion, the hopping height produced by the optimized PI-CPG neural network is more accurate and precise

Optimization of Snake-like Robot Locomotion Using GA: Serpenoid Design

This work investigates the locomotion efficiency of snake-like robots through evolutionary optimization using the simulation framework PhysX (NVIDIA). The Genetic Algorithm (GA) is used to find the optimal forward head serpentine gait parameters, and the snake speed is taken into consideration in the optimization. A fitness function covering robot speed is based on a complex physics simulation in PhysX. A general serpenoid form is applied to each joint. Optimal gait parameters are calculated for a virtual model in a simulation environment. The fitness function evaluation uses the Simulation In the Loop (SIL) technique, where the virtual model is an approximation of a real snake-like robot. Experiments were performed using an 8-link snake robot with a given mass and a different body friction. The aim of the optimization was speed and length of the trace

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Offline GA-based optimisation for heterogeneous modular multi-configurable chained micro-robots

This paper presents a GA-based optimization procedure for bioinspired heterogeneous modular multiconfigurable chained microrobots. When constructing heterogeneous chained modular robots that are composed of several different drive modules, one must select the type and position of the modules that form the chain. One must also develop new locomotion gaits that combine the different drive modules. These are two new features of heterogeneous modular robots that they do not share with homogeneous modular robots. This paper presents an offline control system that allows the development of new configuration schemes and locomotion gaits for these heterogeneous modular multiconfigurable chained microrobots. The offline control system is based on a simulator that is specifically designed for chained modular robots and allows them to develop and learn new locomotion patterns.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid

A Global optimization stochastic algorithm for head motion stabilization during quadruped robot locomotion

Visually-guided locomotion is important for autonomous robotics. However, there are several di culties, for instance, the robot locomotion induces head shaking that constraints stable image acquisition and the possibility to rely on that information to act accordingly. In this work, we propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a genetic algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as ampli- tude, o set and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. Thus, in order to achieve the desired head movement, opposed to the one induced by locomotion, it is necessary to appropriately tune the CPG parameters. Since this is a non-linear and non-convex optimization problem, the tuning of CPG parameters is achieved by using a global optimization method. The genetic algorithm searches for the best set of parameters that generates the head movement in order to reduce the head shaking caused by locomotion. Optimization is done o ine according to the head movement induced by the locomotion when no stabilization procedure was performed. In order to evaluate the resulting head movement, a tness function based on the Euclidian norm is investigated. Moreover, a constraint handling technique based on tournament selection was im- plemented. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that reduces signi cantly the one induced by locomotion