SATBOT I: Prototype of a Biomorphic Autonomous Spacecraft

I n t e r c e p t e d   T r a n s m i s s i o n

SATBOT I: Prototype of a Biomorphic Autonomous Spacecraft

Janette Frigo and Mark W. Tilden
Los Alamos National Laboratory
Copyright © November 27, 1995


[Excerpts]:   Permission to quote sections kindly granted by co-author, M.W. Tilden


Our goal is to produce a prototype of an autonomous satellite robot, SATBOT. This robot differs from conventional robots in that it has three degrees of freedom, uses magnetics to direct the motion, and needs a zero gravity environment. The design integrates the robot's structure and a biomorphic (biological morphology) control system to produce a survival-oriented vehicle that adapts to an unknown environment. Biomorphic systems, loosely modeled after biological systems, uses simple analog circuitry, low power, and are microprocessor independent. These analog networks called Nervous Networks (Nv), are used to solve real-time controls problems. The Nv approach to problem solving in the robotics has produced many surprisingly capable machines which exhibit emergent behavior.[1] The network can be designed to respond to positive or negative inputs from a sensor and produce a desired directed motion. The fluidity and direction of motion is set by the neurons and is designed to orient itself with respect to a local magnetic field; to direct its attitude toward the greatest source of light; and robustly recover from variations in the local magnetic field, power controller for the actuator (air core coil), and two sun sensors (photodiodes) as bias inputs to the neuron. The effect of sensor activation as it relates to an attractive or repulsive torque (directional motion) is studied. A discussion of this system's power (energy) efficiency and frequency, noise immunity, and some dynamic characteristics is presented.

1. Introduction

Most spacecraft developed for scientific, communications, navigation, and military uses are custom designed for the requirements of the payload. This process costs millions of dollars and takes years of development. Furthermore, the investment is jeopardized by subsystem failures, particularly
in the control system.

A space environment demands that the control system be low power, robust, reliable, and autonomous while being able to withstand severe temperature, shock, vibration, and radiation-hardness levels. Over the past fifty years there have been dramatic improvements in methods of designing spacecraft systems; however, every conceivable failure-mode cannot be predicted. Control systems must adapt to failures.
In this paper we investigate an analog neural network controller that locates the autonomy of the control system in the hardware, aims to control the vehicle attitude (motion) for survival, to seek the greatest source of sunlight, and robustly responds to variations in the magnetic field, power, etc. To accomplish this task, a series of prototype robots, SATBOT I, were developed to test the control methodology. We analyze the performance of two prototypes, SATBOT 1.3 and 1.4, by calculating the magnetic torque acting on the vehicle and compare them with other magnetic torque controlled spacecraft.

2. Nervous Networks

Much of control systems and system theory is derived from mathematics such as Lyapunov's Stability Theory. Our approach differs in that it is motivated by biological neurons/nervous systems. Many neural systems in nature are surprisingly adaptive and efficient. Taking advantage of the solutions nature provides, researchers such as Hopfield[2] are building analog models of specific nervous system functions.

An explanation of the elementary function of artificial neurons is useful for the development of the Nv neuron model. The human nervous system is an analog system which consists of approximately ten trillion neurons,[3] each having the ability to receive, process, and transmit electrochemical signals over complex pathways. Neurons are connected by dendrites which extend from cell body to cell body. Signals are received at a connection point called synapse and sent to the cell body. The inputs are summed. The input either excites or inhibits the cell. If the cumulative excitation in the cell body exceeds a threshold, the cell fires, sending the signal to the other neurons. The Nv neuron and most artificial neuron model these simple neuron characteristics.

3. Biomorphic Control Systems

Biomorphic control (from the Greek word biology "to live" and morphology "to take from") systems place an equal importance on mechanical and electrical structure. The structures are an integral part of one another and a deficiency in either hierarchy yields a less successful, less capable device. A biomorphic, autonomous system attempts to (1) achieve directed motion in the environment, (2) procure abundant power, and (3) robustly (even aggressively) negotiate obstacles.[1]

The autonomy of these robots is inherent to their structure. The robot has no knowledge of the surrounding environment and its motion is not controlled by a microprocessor. Rather, an analog computation of the sensory input and the internal process state produces a directed response. The tenuous relationship between weight to power, mass imbalances, coupling and biasing the neuron for dynamic motion are the challenges of designing a biomorphic system.

3.3.2 Neural bias

Next, the sensor placement, and input biases were determined. The sensor location was determined by finding the focal point at which the field of view for each sensor overlaps. There is a solution where equal activation of the sensors at this focal point yields stability.
This difference causes more power in the opposite direction of sensor activation, and a torque in the direction of the light source. The activation can be varied, but as long as one sensor is activated more than the other, the motion will be directed toward the sensor with greatest activation.

3.3.3. Discussion - performance

The system performance shows the responsiveness of our robots to the local magnetic field. This field was chosen for connivance in demonstrating the principal concept. We observed the prototype, SATBOT 1.4, track the greatest source of light in its rotational axis and recover from variations in the magnetic field, power source or the moment of inertia...

5. Conclusion

We designed a functional prototype which can stabilize its position with respect to a local magnetic field and rotate toward the greatest source of light. The system can adapt to variations in the magnetic field, power source, or the structure. The efficiency and power vs. frequency were discussed since the the system can operate in different states as an emergent function of the network and as shown the energy dissipated at these higher frequencies decreases. The noise immunity of the system is due to the fundamental structure of the neuron, a hysteresis differentiator. These characteristics are all advantages in a space environment. Lastly, the dynamic characteristics of this system suggests sufficient torque and power characteristics given the model we have constructed.

References [partial]

[1.] Hasslacher, B., and Tilden, Mark W., "Living Machines," Robots and
Autonomous System: The Biology and Technology of Intelligent Autonomous
Agents, Elsivier Publishers, Spring 1995
[2.] Hopfield, J.J., "Pattern recognition computing using action potential
timing for stimulus representation," Vol. 376, pp. 33-36, 1995
[3.] Wasserman, Philip D., "Neural Computing Theory and Practice," Chp. 1-2,
Van Hostrand Reinhold, New York, 1989

Department of Energy
D.o.E. Information Bridge

SATBOT I: Prototype of a biomorphic autonomous spacecraft, DE96002455, Frigo, J.; Tilden, M.W.; LA-UR--95-3432, 12/31/1995

Los Alamos National Laboratory
November 27, 1995

SATBOT I: Prototype of a Biomorphic Autonomous Spacecraft

Janette Frigo and Mark W. Tilden
Copyright © November 27, 1995
Los Alamos National Laboratory, NIS-1
Los Alamos, New Mexico 87545

See also: A biologically inspired neural network controller
for an infrared tracking system.
Janette R. Frigo, Mark W. Tilden
Los Alamos National Laboratory, MSD448/NIS-4,
Los Alamos, NM, 87545

See also: BEAM Robot Games: Robot Evolution starting at the macro scale.
Mark W. Tilden

See also: BEAM ROBOTICS [Biology Electronics Aesthetics Mechanics]

See also: Boeing
Phantom Works

See also: Borganisation

See also: StarLight FIVE at ...
              ... 3:33

MT © 1999-2000-2013... All Rights Reserved. Return to Intercepted Transmission