Mostafa Ajallooeian

Towards dynamic trot gait locomotion: Design, control, and experiments with Cheetah-cub, a compliant quadruped robot

We present the design of a novel compliant quadruped robot, called Cheetah-cub, and a series of locomotion experiments with fast trotting gaits. The robot’s leg configuration is based on a spring-loaded, pantograph mechanism with multiple segments. A dedicated open-loop locomotion controller was derived and implemented. Experiments were run in simulation and in hardware on flat terrain and with a step down, demonstrating the robot’s self-stabilizing properties. The robot reached a running trot with short flight phases with a maximum Froude number of FR = 1.30, or 6.9 body lengths per second. Morphological parameters such as the leg design also played a role. By adding distal in-series elasticity, self-stability and maximum robot speed improved. Our robot has several advantages, especially when compared with larger and stiffer quadruped robot designs. (1) It is, to the best of the authors’ knowledge, the fastest of all quadruped robots below 30kg (in terms of Froude number and body lengths per second). (2) It shows self-stabilizing behavior over a large range of speeds with open-loop control. (3) It is lightweight, compact, and electrically powered. (4) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.

Modular control of limit cycle locomotion over unperceived rough terrain

We present a general approach to design modular controllers for limit cycle locomotion over unperceived rough terrain. The control strategy uses a Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators as basis. Stumbling correction and leg extension reflexes are implemented as feedbacks for fast corrections, and model-based posture control mechanisms define feedbacks for continuous corrections. The control strategy is validated on a detailed physics-based simulated model of a compliant quadruped robot, the Oncilla robot. We demonstrate dynamic locomotion with a speed of more than 1.5 BodyLength/s over unperceived uneven terrains, steps, and slopes.

Central Pattern Generators augmented with virtual model control for quadruped rough terrain locomotion

We present a modular controller for quadruped locomotion over unperceived rough terrain. Our approach is based on a computational Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators. Stumbling correction reflex is implemented as a sensory feedback mechanism affecting the CPG. We augment the outputs of the CPG with virtual model control torques responsible for posture control. The control strategy is validated on a 3D forward dynamics simulated quadruped robot platform of about the size and weight of a cat. To demonstrate the capabilities of the proposed approach, we perform locomotion over unperceived uneven terrain and slopes, as well as situations facing external pushes.

Fast Hand gesture recognition based on saliency maps: An application to interactive robotic marionette playing

In this paper, we propose a fast algorithm for gesture recognition based on the saliency maps of visual attention. A tuned saliency-based model of visual attention is used to find potential hand regions in video frames. To obtain the overall movement of the hand, saliency maps of the differences of consecutive video frames are overlaid. An improved Characteristic Loci feature extraction method is introduced and used to code obtained hand movement. Finally, the extracted feature vector is used for training SVMs to classify the gestures. The proposed method along a hand-eye coordination model is used to play a robotic marionette and an approval/rejection phase is used to interactively correct the robotic marionettes behavior.

Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2–3 primitives) than kinematic patterns from on-ground locomotion (νm4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware.

Design, implementation and analysis of an alternation-based Central Pattern Generator for multidimensional trajectory generation

In this paper, we introduce a multidimensional Central Pattern Generator (CPG) model with an explicit and defined basin of attraction for generating any arbitrary continuous periodic signal. Having a defined basin of attraction is highly desired, especially in robotic applications, as it provides tracking stability in addition to robustness against disturbances. The CPG model is composed of a set of phase-locked coordinated one-dimensional models; called @z-models. The idea behind the @z-model is generating any one-dimensional periodic signal by altering the behavior of an existing oscillator through two nonlinear maps. The mappings are designed in such a way that the Poincare-Bendixson theorem is satisfied and, consequently, the desired basin of attraction is shaped. The proposed CPG model is extensively tested for generating multidimensional signals; including DC, triangular, and smooth wavy ones. The results show that the CPG model has a low tracking error in addition to being robust against disturbances within the designed basin of attraction. Finally, the proposed CPG model is successfully employed to generate the dancing motion of a situated robotic marionette.

Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform

A novel hybrid evolutionary neural network method to generate multiple spectrum-compatible artificial earthquake accelerograms (SCAEAs) is presented. Genetic algorithm is employed to optimize the weight values of networks. In order to improve the training efficiency, principal component analysis along with some other reduction techniques are used. The proposed evolutionary neural network develops an inverse mapping from compacted and reduced spectrum coefficients to the metamorphosed accelerograms wavelet packet coefficients. As compared to the traditional methods, our algorithm is capable of generating an ensemble of dissimilar 10, 20, 30, and 40 s SCAEAs with better spectrum-compatibility and diversity, and proper computational efforts.

A Dynamic Fuzzy-Based Crossover Method for Genetic Algorithms

Currently, genetic algorithms (GA) are widely used in different optimization problems. One of the problems with GAs is tuning their parameters correctly as they can have a significant effect on GAs overall performance. Till now, different methods have been proposed for fine tuning these parameters. Many of these methods use fuzzy linguistic rules in order to find the correct parameters in each stage of the GA evolution. But these methods look at each chromosome as a whole solution for a specific problem. In our contribution, a new method has been proposed which breaks each chromosome into sub-parts and uses the better sub-solutions as the building blocks of the next generation using a fuzzy-based approach. The performance of this algorithm has been shown on the traveling salesman problem (TSP) with comparison to simple GA and adaptive GA.

An imitation model based on Central Pattern Generator with application in robotic marionette behavior learning

Most of the Central Pattern Generator (CPG) models are based on defining explicit dynamical systems and finding the appropriate parameters. In this paper, we propose a novel CPG model that is based on altering a nonlinear oscillator to obtain desired limit cycle behavior. This CPG model benefits from an explicit basin of attraction and also fast convergence behavior. The presented CPG model is used in an imitation model that tries to learn the proper periodical behavior by looking at a mentor. First, a mentor performs the desired periodical behavior. Then, a hand-eye coordination process, inspired from infant babbling, is initiated to extract proper motor actions from what is observed. The extracted motor actions are finally embedded into the CPG model for smooth reproduction. This imitation model is implemented on a robotic marionette behavior learning task. The outcome of the final performance of the robotic marionette is behaviorally understandable smooth actions.

On designing an active tail for body-pitch control in legged robots via decoupling of control objectives

This work explores the possible roles of active tails for steady-state legged-locomotion. A series of simple models are proposed which capture the dynamics of an idealized running system with an active tail. The models suggest that the control objectives of injecting energy into the system and stabilizing body-pitch can be effectively decoupled via proper tail design: a long, light tail. Thus the overall control problem can be simplified, using the tail exclusively to stabilize body-pitch: this effectively relaxes the constraints on the leg-actuators, allowing them to be recruited specifically for adding energy into the system. We show in simulation that models with long-light tails are better able to reject perturbations to body-pitch than short-heavy tails with the same moment of inertia. Further, we present the results of a one degree-of-freedom tail mounted on the open-loop controlled quadruped robot Cheetah-Cub. Our results show that an active tail can greatly improve both forward velocity and reduce body-pitch per stride, while adding minimal complexity. Further, the results validate the long-light tail design: shorter, heavier tails are much more sensitive to configuration and control parameter changes than longer and lighter tails with the same moment of inertia.