In this paper, we present a walking biped, based on principles of mammalian neuromuscular architecture. Walking in mammals is a fluid, dynamical interaction between a central pattern generator, the biomechanics of the body, the environment, and sensory feedback. Our robot is designed based on principles of human leg muscle architecture. We incorporate load detecting force sensors that model Golgi tendon organs in the muscles, as well as foot pressure and joint angle sensors. These sensory feedback sources model those available in the human body. The robot is controlled by a spiking neuron simulation that integrates centrally generated (CPG) with peripheral (reflexive) responses. Using recent understanding of the neurobiology of locomotion, we are able to generate an effective and stable walking pattern using interactions between the biomechanics, CPG, and reflexive responses. The CPG drives overall limb motion at the hips, while phase modulated reflexive responses adapt the pattern of the lower limb to the needs of the step cycle. Load detection by the force sensors in the limb generates propulsive stepping, and controls entrainment of the CPG through positive force feedback. These concepts are important ones for locomotion in mammals that should be considered by roboticists developing walking robots.

Postural control is a difficult problem for bipedal robots. Even in robots restricted to the sagittal plane, the system must react to falling forward or backward to stabilize itself during walking, standing, and during initiation and termination of walking. Most robots rely mainly on proprioceptive information such as foot pressure sensors and joint angle sensors for balance. By contrast, humans use a variety of sensory sources, including visual, vestibular, and proprioceptive sources to adapt fluidly to varying conditions. These sensory inputs combine to control posture but are "reweighted" in response to changing conditions such as floor motion, visual scene motion, and degradation in vestibular sensitivity. Based on models of sensory reweighting in humans, we implement a sensory reweighting scheme in a bipedal robot using an adaptive Kalman filter. The adaptive filter uses an online estimate of the noise variance to adjust the Kalman gain depending on time-varying noise conditions. Thus, the robot automatically downweight sensory channels with unreliable data.

High speed robot locomotion is one of the most challenging problems in mobile robots. Fast robots push the limits of mechanical design, control and perception. The cheetah is an existence proof of what the authors term an Ultra-High Speed Locomotor, which can attain speeds of greater than 50 leg lengths per second and can cover 10 meters in a single gait cycle. In this paper, the energetics and dynamics of high-speed quadruped movement are analyzed. A leg design is presented that combines a novel hybrid actuator concept plus biarticular muscles to create a lightweight (17 Newton) leg capable of generating 90 Newtons of force. Static and dynamic test results show the promise of the hybrid actuator system in ultra high-speed locomotors.

Address-event-representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for bio-inspired processing systems (for example, image processing). The event information in an AER system is transferred using a high-speed digital parallel bus. This paper presents an experiment using AER for sensing, processing and finally actuating a robot. The AER output of a silicon cochlea is processed by an AER filter implemented on a FPGA to produce rhythmic walking in a humanoid robot (Redbot). We have implemented both the AER rhythm detector and the central pattern generator (CPG) on a Spartan II FPGA which is part of a USB-AER platform developed by some of the authors

We describe in detail the behavior of an inhibitory Central Pattern Generator (CPG) network for robot control. A four-neuron, mutual inhibitory network forms the basic coordinating pattern for locomotion. This network then inhibits an eight-neuron network used to drive patterned movement. We show that we can get predictable control of important relationships such as the phase of the hip and the knee by adjusting tonic parameters. We demonstrate the basic concept both in a simulation that is used to drive a trotting bipedal robot as well as an aVLSI CPG chip that generates spiking burst patterns. Our results indicate that an inhibitory framework can generate simple, understandable and flexible networks for legged robot control that can be implemented in custom VLSI circuits.

We have constructed a second generation CPG chip capable of generating the necessary timing to control the leg of a walking machine. We demonstrate improvements over a previous chip by moving toward a significantly more versatile device. This includes a larger number of silicon neurons, more sophisticated neurons including voltage dependent charging and relative and absolute refractory periods, and enhanced programmability of neural networks. This chip builds on the basic results achieved on a previous chip and expands its versatility to get closer to a self-contained locomotion controller for walking robots. 1

We test the hypothesis that: (1) Optic flow can be used to detect significant environmental features during locomotion in a biped, even given significant up and down movement and jarring of the robot during locomotion. (2) Reliable detection is only possible if a prediction of the expected optic flow field is made at each instance. This prediction should be driven by the phase of the robot's gait as well as other information about the state of the robot. (3) This prediction can be accomplished in a distributed, biologically plausible framework. Our results using a walking biped mechanism strongly support this hypothesis and indicate that optic flow is a viable strategy and that the prediction of optic flow is a critical component in this behavior.

The locomotor controller for walking, running, swimming, and flying animals is based on a central pattern generator (CPG). Models of CPGs as systems of coupled nonlinear oscillators have been proposed and have been used for the control of robots. In this paper we describe the implementation of an adaptive CPG model in a compact, custom analog VLSI circuit. We demonstrate the function of the chip by controlling an underactuated, running robotic leg. This circuit has adaptive properties that allow it to tune its behavior based on sensory feedback. To our knowledge this is the first instance of an adaptive CPG chip. This approach supports the construction of extremely inexpensive, low power and compact controllers for walking, flying and swimming machines.

Existing visually guided walking machines have difficulty traversing terrain cluttered with obstacles. These walking machines use computationally intense approaches that require construction of a geometrically correct model of both the environment and the robot. However, most terrestrial vertebrates accomplish this task easily, suggesting that better strategies exist. We present a model inspired by recent research in cats and humans. In our model, perception and action are tightly coupled. The mapping is adaptive and based on experience. The goal of the adaptation is to use distance measurements to smoothly modulate a central pattern generator (CPG) controlling gait. A key element in our model is the use of a temporal gating hypothesis. This hypothesis simplifies the learning problem and is consistent with biological observations. Our approach does not require that a geometric representation of the environment be created or updated based on new observations. This is in strong contrast to current practice in machine vision and robotics of surface reconstruction as a prerequisite to planning. Our simulation results indicate that the desired mapping can be learned quickly with few mistakes before perfect performance is achieved. The resulting gait modulation is smooth and coordinated with the phase of the CPG controlling the robot.

A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. To address this problem, the concept of a virtual structure is introduced. Control methods are developed to force an ensemble of robots to behave as if they were particles embedded in a rigid structure. The method was tested both using simulation and experimentation with a set of three robots. Results are presented which demonstrate that this approach is capable of achieving high precision movement which is fault tolerant and exhibits graceful degradation of performance. In addition, this algorithm does not require leader selection as in other cooperative robotic strategies. Finally, the method is highly flexible in the kinds of geometric formations that can be maintained.

This paper describes steps toward making a collection of robots easily accessible to remote users. The current approach is to standardize the interface to an existing robot, provide connectivity to the Internet and provide a Universal Interface to remote users. We leverage existing system software and operating systems to create an easily programmable, flexible system. By providing the system with certain tools, the experimenter is able to automatically create a record of the experiment for future analysis.

Many current avenues of vision research involve fully analyzing an image with expensive, high powered computers. This approach has major implications in terms of cost, size, and power consumption. Other methods have involved sub-sampling an image to reduce cost and complexity. This has the disadvantage of information loss. We present a low cost, low powered, reduced complexity vision system capable of intelligently sampling an image to reduce this information loss. The design philosophy and methodology is discussed, along with sample applications. Primarily we demonstrate how the reduced complexity vision system will be used to aid in navigation of an autonomous flying vehicle. This is quantified by showing how having multiple sampling schemes result in increased robustness and accuracy of our helicopter line tracking algorithm.

A methodology for the mobility evaluation and mission oriented performance assessment of an autonomous mobile robot is developed. The evaluation strategy uses both time and energy based measures resulting in the development of isochronal/isoenergy contours which characterize the expected mobility of the robot over novel terrain using sample trials on patches of land prior to the mission. This work has a basis in the evaluation of a robot for extraterrestrial exploration and demonstrates the feasibility of the evaluation methodology proposed using simulations. Simulation results are reported for two designs of micro-rovers viz. legged and wheeled on a simulated Martian terrain. Future work is discussed in the context of increased complexity due to hardware implementations, better terrain models and statistical simulations all of which are currently under development.

This article describes the design of an 8 degree of freedom snake-like robot, the R7 manipulator. The purpose of the device is to position a CCD camera and to rapidly acquire a sequence of images in an environment containing obstacles that may constrain the motion of the robot. Because of these requirements, it was desirable to minimize the manipulator's size and maximize its speed while preserving accuracy. These goals led to a unique design that is presented here. The device consists of four transmission modules. Each module has 2 degrees of freedom and contains a dual differential drive mechanism. Each module is a little more than 1.0" in diameter. The coordinated motion of these differential drives produces a prehensile motion of each transmission module. The range of motion of each module is /spl plusmn/90/spl deg/ in yaw and pitch. The smooth prehensile bending easily accommodate fragile cables and wiring harnesses. A benefit of using rigid links, as in this design, is an increase in stiffness of the mechanism. High stiffness aids in rapid positioning. One key feature of the mechanism is the use of low tension cable drive. Cables pass over a series of stationary sheaves and transmit power to transmission units located in each module. The use of a gear reducing transmission allows the use of low tension cables. This paper describes the design of the R7 manipulator and supporting electronics. Also discussed is the numerical solution to the inverse kinematics for this novel manipulator. The robot was built at Hughes Aircraft.>

A control system architecture is described for an autonomous flying vehicle. The vehicle, equipped with fourteen sensors, uses a model helicopter as an airframe. The control system utilizes these sensors to: remain aloft and in stable flight; navigate to a target; and manipulate a physical object. The overall approach to the problem is based on a behavioral paradigm. The key contribution of this paper is the demonstration of a situated agent under these severe circumstances; as the craft is airborne, it is in constant risk of crashing. Unlike terrestrial mobile robots, the craft must constantly make sound decisions to maintain its integrity.

The authors describe the staged evolution of a complex motor pattern generator (MPG) for the control of a walking robot. The experiments were carried out on a six-legged, Brooks-style insect robot. The MPG was composed of a network of neurons with weights determined by genetic algorithm optimization. Staged evolution was used to improve the convergence rate of the algorithm. First, an oscillator for the individual leg movements was evolved. Then, a network of these oscillators was evolved to coordinate the movements of the different legs. By introducing a staged set of manageable challenges, the algorithm's performance was improved.>

The behavior of a colony of p-robots, with the common goal of destroying malignant tissue in the brain, is simulated. The behavior of each robot is gov- erned by local rules of interaction with the environ- ment. Each robot alone has a low probability of achiev- ing its purpose. Collectively, the synergistic effect of cooperation significantly improves the likelihood of an individual achieving its purpose. The behavioral strat- egy to achieve such cooperation (by means of chemical communication signals) is described. The method is il- lustrated by simulation results. I. INTRODUCTION

R/sup 2/AD, a flexible robotics application development environment, combines powerful graphical display features with tools that support the rapid development of application programs. The graphical interface allows researchers to monitor the system state variables during the dynamic execution of robot programs. This feature enhances the researcher's efficiency in interpreting experimental results. Rapid development is supported by a virtual machine through the use of software engineering principles and object-oriented techniques. In addition to these features, the user is able to emulate a wide variety of robot computational paradigms. This allows experimentation with, and comparison of, various computational techniques (e.g., hierarchical versus heterarchical control process topologies). The utility of the R/sup 2/AD virtual machine is demonstrated through applications currently under development.>