Biomimetic Underwater Robots

A Modular Behavioral-Based Architecture for Biomimetic Autonomous Underwater Robots

Biological Control Systems

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The locomotory and taxic behaviors of animals are controlled by mechanisms that are conserved throughout the animal kingdom (Kennedy and Davis, 1977; Stein, 1978). The neuronal mechanisms underlieing locomotion were initially established by study of simple animals including lobsters and crabs, insects, sea slugs and worms (Hoyle, 1976; Kennedy and Davis, 1977). These mechanisms have been formalized into a general model, the command neuron, coordinating neuron, central pattern generator model (CCCPG model, Fig. 2; see also Kennedy and Davis, 1977; Stein, 1978). The model is composed of five major classes of components including central pattern generators (Pinsker and Ayers, 1983), command systems (Kupferman and Weiss, 1978), coordinating systems (Stein, 1976), proprioceptive and exteroceptive sensors (Wiersma, 1977) and phase and amplitude modulating sensory feedback (Stein, 1978).

The fundamental governing concept of the CCCPG model is that the motor output that underlies behavior is generated by genotypically specified central pattern generators that are modulated by peripheral exteroceptive and proprioceptive feedback during behavior (Delcomyn, 1982). In other words the central nervous system can generate central motor programs in the absence of sensory feedback. This central pattern generation model differs fundamentally from reflex-chain models where sensory feedback is necessary to specify transitions between different phases of a cyclic behavior (Sherrington, 1906). The central component consists of :

Segmental central pattern generators (CPGs) that control the motor neurons and ultimately the muscles of each limb

Coordinating systems that determine the phase relations or gaits between the CPGs of different limbs

Command Systems that specify and modulate the behavior generated by the CPGs. The command systems represent the control locus at which the decision to generate a particular behavior is achieved.

Fig. 2. Command Neuron, Coordinating Neuron, Central Pattern Generator model of locomotory systems. A. Configuration of components in an ambulatory ;system. B. Configuration of components in an undulatory system. Abbreviations: CPG: central pattern generator; CN: coordinating neuron; Ext: extensor synergy of motor neurons; Flx: Flexor Synergy.

The peripheral component consists of exteroceptive and proprioceptive sensors that provide feedback to the central pattern generators to generate:

Exteroceptive or Orientational Reflexes that operate at the level of the command systems to generate whole-body compensatory responses

Phase Modulating Reflexes that operate at the CPG level to reset the timing of oscillations during stumbles, etc. and

Amplitude Modulating Reflexes that operate at the motor neuron level to control the amplitude of the motor output.

The neuronal control mechanisms of insects and decapod crustacea (cockroaches, locusts, lobsters, crayfish and crabs) have been subjected to considerable reverse engineering over the past 25 years, adequate to permit robust synthetic models of their underlieing organization (Beer et al., 1992). In several cases the actual synaptic networks have been established by electrophysiological stimulation and recording (Pearson, 1976; Chirachri and Clarac, 1989). In fact the resulting neuronal circuit based models can achieve much of the complexity that underlies higher order behavior (beacon tracking, Beer, 1991; adaptive walking in different directions, Ayers and Crisman, 1992). These biological models can be readily adapted to robotic control (Brooks, 1992; Beer et. al; 1992; Ayers and Crisman, 1992). We submit that biologically-based reverse engineering is the most effective procedure both to design autonomous underwater robots as well as to establish detailed higher order control schemes for procedures such as remote sensing and mine countermeasures.

Figure 3 Upper Panels: Lobster Sensors. Upper left: Hair fan water current receptor. Upper right: Statocyst balance organ. Lower panels: MEMS Sensors. Lower left: current receptor, Lower right: Inclinometer

Fig. 5. Finite state Analysis of animal behavior. A. Digital movie of lobster behaving in an aquarium. B. Finite-state diagram indicating state changes of the different task groups during behavior. C. Radio-button panel used in constructing the state diagram shown in Fig. 5b from movies such as shown in 5c. Courtesy of Lars Schlichting

The second major component of the ambulation controller is the pattern generator that determines the pattern of discharge of bifunctional synergies. The pattern generator responds to the state transition message and desired period parameter from the oscillator, polls the walking command logic and determines through a truth table which synergies should be active and sets or clears the booleans associated with different bifunctional synergies. The truth table implements the presynaptic inhibitory logic of our neuronal circuit model and specifies the excitatory connections that will be disabled by the directional command.

Biologically-Based Undulatory Robots

The swimming behavior of fishes ranges in organization from anguilliform ( relying on lateral axial undulations, Fig. 9a) to carangiform (relying on a flapping tail and/or fins). Anguilliform locomotion is common in eels and lamprey. As described above, we have developed a multi-media analytical system for reverse kinematic analysis of lamprey swimming (Ayers and Fletcher, 1990; Ayers, 1992). Anguilliform swimming results from propagation of flexion waves from the anterior region of the body to more caudal regions (Fig. 9). During anguilliform locomotion, the propagation time of the waves from nose to tail is equal to the period of the undulations so that the body axis typically exhibits an S shape.

Propagating flexion waves alternate on the two sides to generate undulations. The amplitude and timing of the axial undulations are controlled independently (Ayers, 1989). Swimming behavior is controlled on a flexion wave by flexion wave basis. Turning and other maneuvering actions are mediated by modulation of the amplitude of individual flexion waves.

The thrust generated during anguilliform swimming is pulsatile (Fig. 9c). Peaks of thrust are generated as flexion waves propagate to ~65% of body length and are correlated with maximal unbending of the body axis. Our working hypothesis is that the magnitude of thrust is regulated by modulation of axial stiffness by coactivation of musculature on the two sides of the body. Control of axial stiffness is necessary to adapt the speed of locomotion from low search speeds to more rapid pursuit behavior

Fig. 9 Undulatory movements and thrust production during anguilliform swimming. A. Curvature analysis of the locus of lateral flexions. B. Timing of undulatory movements. In this graph each point represents the locus (as a % of body length from nose to tail) of flexions in each of the frames of a movie. Propagating flexions on each side are grouped into flexion waves that propagate from nose to tail. Lower panel: Simultaneous swimming thrust registered with a force transducer tethered to the body at 25% of body length.

Neural Control of Undulatory Locomotion

Numerous physiological studies have demonstrated that undulatory locomotion is generated by segmental central pattern generators (Grillner and Wallen, 1984) that are coordinated by contralateral and ipsilateral coordinating systems (Fig. 2b). The central pattern generators in turn activate motor neurons that are recruited in order of size to grade the intensity of contractions and the resultant lateral flexions (Grillner and Kashin, 1976).

Neural Circuit-Based Controller

The necessary control signals are easily generated with a minor modification of the finite-state machine used for ambulatory control (Ayers and Crisman, 1992). In undulatory systems lateral flexion signals are sent sequentially to segmental muscles, thus the undulatory controller requires only the clock and recruiter levels of control present in the ambulatory controller (Fig. 10a). We are evaluating SMA wires and similar technologies as linear actuators to mediate axial undulations. Nitinol wires of diameters as small as 50µ can generate tensions of up to 30 grams. Arrays of such wires are activated by the controller to generate undulatory movement by sequentially activating flexions over different body regions (Fig. 10). The controller is implemented on a sequential processor which sets and clear digital output signals. The actual control signals will be strobed to a set of shift registers, each bit of which will gate a set of transistors that control current to the actuators (Fig. 11). Thus the actuator control signals will be regularly reset at the time of state changes in the controlling program.

Fig. 10. A. Overall organization of undulation controller. Segmental oscillators are connected by contralateral and ipsilateral coordinating elements. The oscillators in turn activate linear actuators that flex different regions of the body axis. B. Schematic diagram of an undulatory robotic system C. Activation patterns of segmental actuators during slow and rapid swimming. Each trace in the two panels indicates the activity status of different quartile actuator wires. D. Operation of a prototype undulatory actuator system (After Jalbert et al., 1995)

link to original research webpage:

http://www.dac.neu.edu/msc/burp.html