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

Unified Behavior Framework in an Embedded Robot Controller

Robots of varying autonomy have been used to take the place of humans in dangerous tasks. While robots are considered more expendable than human beings, they are complex to develop and expensive to replace if lost. Recent technological advances produce small, inexpensive hardware platforms that are powerful enough to match robots from just a few years ago. There are many types of autonomous control architecture that can be used to control these hardware platforms. One in particular, the Unified Behavior Framework, is a flexible, responsive control architecture that is designed to simplify the control system’s design process through behavior module reuse, and provides a means to speed software development. However, it has not been applied on embedded systems in robots. This thesis presents a development of the Unified Behavior Framework on the Mini-WHEGS™, a biologically inspired, embedded robotic platform. The Mini-WHEGS™ is a small robot that utilize wheel- legs to emulate cockroach walking patterns. Wheel-legs combine wheels and legs for high mobility without the complex control system required for legs. A color camera and a rotary encoder completes the robot, enabling the Mini-WHEGS™ to identify color objects and track its position. A hardware abstraction layer designed for the Mini-WHEGS™ in this configuration decouples the control system from the hardware and provide the interface between the software and the hardware. The result is a highly mobile embedded robot system capable of exchanging behavior modules with much larger robots while requiring little or no change to the modules

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments

Design Issues for Hexapod Walking Robots

Hexapod walking robots have attracted considerable attention for several decades. Many studies have been carried out in research centers, universities and industries. However, only in the recent past have efficient walking machines been conceived, designed and built with performances that can be suitable for practical applications. This paper gives an overview of the state of the art on hexapod walking robots by referring both to the early design solutions and the most recent achievements. Careful attention is given to the main design issues and constraints that influence the technical feasibility and operation performance. A design procedure is outlined in order to systematically design a hexapod walking robot. In particular, the proposed design procedure takes into account the main features, such as mechanical structure and leg configuration, actuating and driving systems, payload, motion conditions, and walking gait. A case study is described in order to show the effectiveness and feasibility of the proposed design procedure

Classification and Identification of Environment Through Dynamic Coupling

This paper presents a methodology enabling robotic systems to classify and identify their environment according to the mechanical properties of the local contact dynamics. Described approach employs existing proprioceptive sensors and requires no additional specialized hardware. Identification process is performed in real-time with temporal resolution of measurement updates determined by the periodicity of the limit behavior. While the basic concept has a wide application spectrum, our discussion focuses on terrestrial locomotion where contact properties, such a compliance, damping, sheer friction and surface topology, are important environmental markers. Accurate identification of environmental parameters enables two types of applications. In behavioral control, availability of measurements on environmental parameterization can facilitate better adaptation of actuation strategy. In localization and map building applications, such mechanical characteristics of the environment, which are typically hard to attain, can serve as a new set of classifiers. Presented approach is founded on the observation that locomotive behaviors, and particularly the dynamic ones, emerge from the interaction between the active actuation actions of the mechanism with its environment. To evaluate our concept in a systematic fashion we constructed a simplified numerical model of a dynamic hexapod robot. We present results on numerical simulations and outline a path for a physical implementation on dynamic hexapod robot

Biologically Inspired Climbing with a Hexapedal Robot

This paper presents an integrated, systems-level view of several novel design and control features associated with the biologically inspired, hexapedal, RiSE (Robots in Scansorial Environments) robot. RiSE is the first legged machine capable of locomotion on both the ground and a variety of vertical building surfaces including brick, stucco, and crushed stone at speeds up to 4 cm/s, quietly and without the use of suction, magnets, or adhesives. It achieves these capabilities through a combination of bioinspired and traditional design methods. This paper describes the design process and specifically addresses body morphology, hierarchical compliance in the legs and feet, and sensing and control systems that enable robust and reliable climbing on difficult surfaces. Experimental results illustrate the effects of various behaviors on climbing performance and demonstrate the robot\u27s ability to climb reliably for long distances

Disturbance Detection, Identification, and Recovery by Gait Transition in Legged Robots

We present a framework for detecting, identifying, and recovering within stride from faults and other leg contact disturbances encountered by a walking hexapedal robot. Detection is achieved by means of a software contactevent sensor with no additional sensing hardware beyond the commercial actuators’ standard shaft encoders. A simple finite state machine identifies disturbances as due either to an expected ground contact, a missing ground contact indicating leg fault, or an unexpected “wall” contact. Recovery proceeds as necessary by means of a recently developed topological gait transition coordinator. We demonstrate the efficacy of this system by presenting preliminary data arising from two reactive behaviors — wall avoidance and leg-break recovery. We believe that extensions of this framework will enable reactive behaviors allowing the robot to function with guarded autonomy under widely varying terrain and self-health conditions