Con fecha 24 de agosto del 2017, reunidos en Pleno los Integrantes de la Comisión de Hacienda, aprobaron el presente Dictamen

The MISO convolution model was effective for understanding how to predict of

propulsive force from a compliant, multiple degree of freedom robotic fin with complex

dynamics. It was effective for prediction of forces from sensory data and provided insight

into the underlying relationships between sensor parameters and forces. The MISO model

structure allowed for an in depth analysis of the roles of individual sensors, the memory

of the system, and the performance effects of changing multiple parameters. The

underlying model could be further improved using basic knowledge of robot behavior

(e.g. which gait and stroke phase are being executed). The evaluation methods used,

including MISO for prediction and AIC for sensor and parameter selection, suggest a

framework with which to optimize sensor selection and placement for intrinsic robotic

research areas, including: multi-modal tactile sensing in perception research [84, 115],

grasping studies of in-hand manipulation [116, 117], and proprioceptive robotic systems

for measurement of human body forces and kinematics [118]. The empirical techniques

presented here could be used to validate optimal sensor modalities and optimal sensor

placements for many other sensing challenges in robotics, especially where the dynamics

of the system are challenging to model mathematically but feasible to model with bio-

inspired robotic platforms [119]. These techniques could be valuable to the study of

animal systems, where compliance, complex dynamics, and environmental effects all

contribute significantly to the performance of particular behaviors.

Complex changes to the fin kinematics required more complex sensing and

modeling strategies. When small changes were made to the fin dynamics, by changing

flapping frequency or fin stiffness, the same model could be used to predict forces across

these conditions (Figure 39). But when the entire swimming gait changed, the model

trained on one gait could not be used to predict the forces of another gait. For instance,

models trained on steady swimming forces were unsuccessful at predicting the forces of

ventral steady swimming (Figure 42), even though ventral steady swimming has the same

ventral edge kinematics as steady swimming (Figure 37). Sensing strategies also had to

change as gaits changed. The best sensors subsets for each gait differed, as best

predictions of ventral steady swimming forces were achieved with S={C,CO,A,B} and best predictions of steady swimming forces were achieved with S={BO,A}. The fluid structure interaction that produces propulsive forces involves multiple fin regions and the

energetic exchange of their interactions [70, 120], and so large changes to kinematics can

sensory system that can reliably predict forces when swimming gaits change, multiple

sensors and models are needed. This may have implications for robots that execute

complex gaits and gait changes with compliant control surfaces, such as elastomer-based

soft robots that crawl and undulate [121], fish-inspired robots that use compliant fins to

execute multiple swimming gaits [33, 109, 122], and bipedal robots with compliant joints

that shift from walking to jogging [123]. Robots that engage in multi-modal locomotion

(e.g. aquatic to terrestrial [124, 125], terrestrial to aerial [126], aerial to scansorial [127])

may also benefit from multi-modal, distributed sensory systems; because as the physics

of the environment change, the underlying sensor modes and distributions may have to

change accordingly.

The best sensory modalities were related to known dynamics of compliant fins.

Bending sensation was more useful for force prediction than pressure sensation in the

compliant, multi degree of freedom, robotic fin (Table 7). Bending and compliance have

been shown to play a major role in force production in fins, and good estimation of the

forces occurred using distributed strain measurements. Past study of bending and

curvature in sunfish pectoral fins show that the fish can modulate fin stiffness [16, 17].

Since stiffness control is a major mechanism of force production in fins [17], it is

reasonable that bending would be highly useful for force prediction, as demonstrated

above empirically. The importance of bending measures in fins is consistent with

behavioral biology studies [3] and neurobiological evidence [6]. Theories of haptic

function have argued that local strain measures may be more informative for contact

sensing than pressure measures and may be what is more commonly found in biological

from computational biology, neurobiology, and haptics disciplines can provide insight

into sensory instrumentation in robotic systems. However, in this study, bending data

alone was not sufficient to predict propulsive forces in all robotic swimming modes

(Table 6), so propulsive force prediction may perform best using multiple sensory

modalities, such as: fin ray bending, fin pressure, membrane stretch, and further

components of the strain.

The placement of sensors is important for force prediction. Good placement may

agree with the areas responsible for force production in animal and robotic models.

Sensors along the dorsal leading edge, both bending and pressure sensors, were essential

for good prediction of propulsive forces. The importance of the dorsal leading edge in

thrust force production has been well documented in study of bony-finned fishes [11, 32].

Since sensor placement was determined primarily by study of steady swimming modes

[70], it was consistent that model performance was highest for predicting force

magnitudes during steady swimming. Further, biological evidence suggests that free

nerve afferents in sunfish pectoral fins innervate multiple regions of the fin and have

relatively high density in the dorsal and ventral leading edges [6, 19, 20]. In this way,

engineers may be able to look at simulation data and biological studies of relevant animal

gaits in order to determine initial locations for sensors on their biologically-derived

robots.

Distributed, heterogeneous sensors in fins can serve multiple roles in the control of

propulsive force: they can be used for forming accurate predictions of the force (Figure

39), can serve as direct inputs to kinematic controls, and can be exploited for more robust control with redundancies. In some cases, the sensory data could be used to predict the

propulsive force in advance (60-70ms; Figure 44), which could be useful in a

feedforward control framework. While sensory data can be fused to predict forces, the

individual sensor components are still available to the operator and can be used in a

control framework for fast updates to kinematics. Intrinsic sensors provide information

about the strain and pressure distributions across the fin’s control surface, which can aid

in understanding the components of the propulsive force; whereas this local information

about force is largely unavailable to a single extrinsic sensor (such as a force sensor at the

fin-body interface). Having access to local components of the propulsive force (local

strains, pressures) can be exploited by a controller that updates local kinematics

according to these measures. Using both the force predictions and the sensory data

directly provides more feedback pathways in a control framework. Intrinsic sensors

provide redundant information due to the mechanical coupling created by the fin

webbing. Redundant sensors can be used to help localize contact with an obstacle (as in

terrestrial examples of whisking [65, 66]), to compensate for a sensor failure by sampling

from surrounding sensors, or to provide weighted estimates of the force that improve on

estimates from single sensors or single modalities (c.f. robotics research in hyperacuity

[84]). The redundancy gained by through intrinsic, distributed, heterogeneous sensors can

be exploited for more robust estimates and more robust control. These advantages are

ripe for exploration in future work with biologically-inspired robots.