Towards an All-Polymer Robot for Search and Rescue Robert A. Nawrocki
Sean E. Shaheen
University of Denver University of Denver Dept of Comp Eng Dept of Phys and Astro 2390 S. York St 2112 E. Wesley Ave Denver, CO, USA Denver, CO, USA
[email protected] [email protected]
Abstract—This paper discusses two components suitable for construction of an all-polymer robot, namely a Synthetic Neural Network and water hammer based actuation. A new data processing element, termed Synthetic Neural Network, or SNN, based on a concept of a polymer-based bistable memory device and a conventional transistor made from polymers, is proposed. A phenomenon known as the water hammer effect is described for the purposes of propulsion of the serpentine robot constructed from polymer tubing. Arresting the flow of water in the tube causes it to lurch forward. A relationship between the shape of the hose and the direction of propulsion is investigated with the goal of using the SNN to learn to control the forward progress of the robot based on polymer bend sensors. Keywords: polymer robots, water hammer, synthetic neural networks, parallel data processing, polymer electronic I. INTRODUCTION The field of robotics is commonly associated with large, industrial machinery designed for tasks such as automobile assembly. In recent years robots have entered such diverse fields as robotic surgery [1]-[3], and domestic robotics [4]-[6]. Researchers have pushed to the limits their imaginations trying to fulfill dreams of non-rigid or flexible robots such as the liquid metal robot in James Cameron's movie called “Terminator 2: Judgment Day.” A robot not bounded by its rigid structure would possess great benefits over a robot that is encased by a non-flexible shell. Our lab has constructed small robots with particular accessibility characteristics, such as the TerminatorBot [7]-[8], constructed for search and rescue missions in collapsed structures. The TerminatorBot is a soda can-size robot intended for deployment into voids through a bore hole. However, in situations where the path is too narrow or a turn too sharp, even such a small robot cannot move forward through the obstacle. Rodents, on the other hand, are quite adept at navigating obstructed and serpentine pathways and a robotic design featuring a flexible skin or non-rigid body would possess an obvious advantage in such circumstances. Plastics, or polymers, are a group of materials that are known to possess
Xiaoting Yang
Richard Voyles
University of Denver Dept of Comp Eng 2390 S. York St Denver, CO, USA
[email protected]
University of Denver Dept of Comp Eng 2390 S. York St Denver, CO, USA
[email protected]
both strength and flexibility and as such appear to be a perfect candidate for constructing a non-rigid robot. A number of solutions for construction of an all-polymer robot already exist. Conducting polymers have been used as piezoelectric sensors for strain gauges as well as actuators used for contraction and expansion [9]-[13]. Also, a number of research groups have demonstrated complete robotics systems based in polymers [14]-[15]. In [14], Yeom et al. demonstrated a biomimetic jellyfish robot created with ionic polymer metal composite that mimics the real locomotive behavior of a jellyfish. Sameoto, in [15], created an all-polymer foot capable of climbing walls, mimicking that of a gecko or spider foot. Both of these designs were inspired by biological systems. Polymer-based chemicals used as fuel [16]-[17] were developed in the mid 1990s, though they haven't gained much commercial success. Polymer or polymer-based sensors are either proposed or already used for sensing normal and sheer forces and as biological sensors for detection of analytes [18]-[21]. This paper presents on-going research aimed towards developing an all-polymer robot. Work presented in this paper concentrates on development of all-polymer information-processing hardware along with alternate means of propulsion. Polymer bend sensors and actuation via muscle-like methods are part of our long-term vision, but are not addressed in this paper. II. INDIVIDUAL COMPONENTS Every system, regardless of how simple or complex, can be broken down to its individual components. This paper, therefore, is arranged to address two main parts that would comprise the all-polymer robot: information processing and propulsion. A. Information Processing – Synthetic Neural Network For every system interaction with the environment is essential. Every robot, hence, needs to process information it receives from the environment. Currently, the most ubiquitous form of information processing is carried out by some form of a
Central Processing Unit, or a CPU. Today's CPUs are fast, cheap and relatively easy to program. However, they consume a lot of energy, produce significant amount of heat, and process information in a serial fashion. As such they are suitable for some tasks but not all. A truly parallel processing unit would circumvent shortcomings of a serial CPU. Additional benefit of a distributed , NN architecture is a capacity of much higher processing power per watt of power consumption, at least for problems that are amendable to such architecture. Our work concentrates on developing a hardware ANN, as an information processing element, constructed using polymer or synthetic material, hence we use the term Synthetic Neural Network to explicitly differentiate it from serially-emulated artificial neural networks.
However, once a threshold voltage is reached, the device jumps to a higher current level, ION. A finer granularity of the connection weight can be achieved by incorporating a matrix of such memory elements. A subset of those elements in the ON state would determine the value of the connection weight. The neuron itself is constructed from a differential amplifier with a non-linear activation function. All of the cells are arranged in a plane with wires connecting all of the inputs and outputs of the cells. Input signal is connected to all of the input wires and the output signal is received from all of the output wires which results in a massive parallelism. Memory cells
A1. CrossNet Recently a very promising technology for producing small-scale electronics with a novel way of connecting them that offers fault tolerance has been proposed. CMOL (CMOS-MOLecular hybrid) CrossNet architecture, proposed by Likharev [22]-[25] is based on CMOS devices that are arranged in a CrossNet and connected using nano-scale wires. In CMOL a layer of two-terminal devices is used as an add-on to a CMOS subsystem [22]. The individual devices are connected in such a way as to form a hardware based Artificial Neural Network. Likharev et al. [22] have demonstrated, though only theoretically, the possibility of arranging the circuit as a feedforward network (see Figure 1) as well as a recurrent network with training by error backpropagation and Hopfield-mode guidance. Türel et al. [23] have also demonstrated that their CMOL CrossNet can be successfully used for image analysis/recognition as well as pattern classification.
Voltage gradient
Figure 2. Connection of individual memory cells needed for creation of a single artificial neuron. A combination of Vh and Vv,creates a gradient, marked as light gray triangle, which determines a subset of individual memory cells.
Input
A2. Memory Device as a Synapse Synapse (Memory)
Neuron (Op Amp)
Output
Figure 1. Feedforward architecture of CMOL CrossNet. Light squares, marked SNN cells, denote a single cell shown in Figure 2. They are connected with input and output wires.
The network consists of four elements: input and output wires, neurons and synapses. In a simplest case synapse is constructed from a single, two terminal, bistable memory device, producing a binary connection weight. Within a certain voltage range this device produces a low current, IOFF.
Synapses for our artificial neurons are constructed of a matrix of memory elements, as described above. These memory elements are two-terminal, bistable memory devices. A number of research groups have demonstrated a successful construction of such devices possessing both the necessarily high ON/OFF ratio and long-term retention [28]-[34]. However, all of these devices are made with metals. Depositing metal requires metal deposition in a vacuum chamber and, therefore, is expensive and time consuming. Our work aims at creating a memory device that utilizes other materials, hence alleviates the need for use of a vacuum deposition. Our device has already been demonstrated to possess the necessary quality of a binary state, ON or OFF, which is facilitated by the existence of a hysteresis curve on an I-V plot. However, the ON/OFF ratio needs to be improved. Also, analysis pertaining to monitoring long-term performance of such a device still remains to be conducted with the
aforementioned remaining as inconclusive for a long-term viability. The technology suggested by Likharev et al. is based on the currently available CMOS technology. However, any latching device, that is an electrically bistable device, can be employed for the purposes of creation of an artificial neuron. The polymer-based memory device that we created utilizes a combination of poly(methyl 2-methylpropenoate) (PMMA) and a fullerene derivative [6,6]-phenyl-C61-butyric acid methyl ester (PCBM). Figure 7 presents the I-V curve of the observed ON/OFF current with associated read/write voltage. It can be seen in Figure 3 that the ON/OFF ratio is more than one order of magnitude apart. This clearly needs to be improved in order to ensure proper and safe functioning of mass produced technology. Also, as already mentioned, a long-term retention study of our device needs to be conducted. ON
OFF
Figure 3. An example of an I-V curve with the corresponding ON and OFF currents.
A3. Op Amp as Neuron As already mentioned, neuron is formed from a differential Op Amp with a non-linear activation function. A number of research groups have demonstrated successful creation of organic transistors [53], [54]. At present we're concentrating on optimizing our memory device before we can move on to organic transistors. A4. Virtual Training ANNs have been successfully employed in such diverse functions as prediction of performance of a stock market [51], fraud management and loan defaulting in the banking industry [50], control and sensor monitoring in industrial plants [47], memory allocation in embedded systems [48], and target detection in vision-based systems [49]. However, because ANNs are parallel processing concepts emulated on serial machines, their speed of operation is rather slow hence most analysis and applications relating to ANNs pertains to either off-line systems or non-motion or non-mechanical systems. For our proposed all-polymer robot, the SNN we are developing will be the real-time control center for mapping the
shape of the polymer tube actuator to the resultant directional impulses of the water hammer effect. (The water hammer effect is discussed in details in the next section.) In other words, it will answer the question, “If the tubing is lying along curve S, what will be the vector direction of a force impulse acting on the valve?” Eventually, we plan to explore the inverse question which is, “If we want to effect an impulse of direction X, how can we actuate the water hammer, given curve S, to achieve this?” Our SNN, hence, will be a hardware, parallel processing system, and, as such, we conducted a study pertaining to feasibility of control of a mechanical system using ANN. The experiment was conducted in a virtual environment which allowed for reduced cost and increased speed in obtaining the results. The network was trained to map the inverse kinematics (mapping the position of the end of the arm, which exists in a 3D Cartesian space, into joint-angle space of the individual links of the robotic arm) which is usually represented by Jacobian matrix. Joint angles, represented by circles in Figure 4, represent bend sensors detecting the current configuration of the arm. Our experiment, discussed in detail in the next section entitled Directed Propulsion, demonstrates that shape is critical for direction of propulsion. The shape can be described at bending points, therefore information collected at these points represents the shape. We used information collected using inverse kinematics but, in a live experiment, this information would be collected by polymer bend sensors and used as the input data for SNN during both training and subsequent operation. Figure 4 presents a snapshot of the robotic arm that was trained on a feedforward network, with a single hidden layer consisting of 100 cells, using backpropagation as the training algorithm, with the arm trained to a high level of precision (the precision, or error, can be seen in the figure as the difference between the desired location and the actual location). The experiment was conducted using MATLAB as the computational environment for the neural network [46]. The arm was constructed by Demura [45] and controlled using C language incorporating ODE, or Open Dynamics Engine [44], which is a set of libraries providing for physically realistic simulations. The accuracy of the training can be observed in Figure 4 as the difference between Actual Position and Desired Position. The figure demonstrates successful outcome of the experiment. Artificial Neural Networks train by continuously adjusting connection weights of individual neurons. In a virtual environment such a precision is almost limitless, bounded only by the limitation of the software or the operating system. Commonly, however, numbers are rounded off to just a few decimal places. A precision of a connection weight of a single neuron as a part of an SNN is determined by the number of memory elements comprising a single neuron. Hence achieving accuracy of two decimal places would require employment of 100 individual memory cells for a single neuron. This demonstrates a tradeoff between the accuracy of
connection weights, which relates to the speed of training, and the overall number of connection weights. However, Likharev demonstrates that even networks with binary connection weights, or using a single memory element for a single synthetic neuron, would produce network capable of training and successful application for various tasks. This experiment, however, will be repeated with network architecture and learning algorithm representing the expected conditions of SNN.
in a situation that resulted in the tether being stuck. Two experiments were conducted with the tether wrapped around two cylindrical objects, forming an S curve and the tether being stuck underneath a door. In both situations the jittery movement of the hose, caused by water hammer effect, resulted in the tether being set loose enough for the small car to pull it. A second set of experiments aimed at identifying if the water hammer effect could be used, exclusively without any external power, with the object placed at the end. The experiment demonstrated that with a straight hose the cart would be propelled directly along the direction of the hose. The effect was substantial enough to deem it feasible as a sole source of propulsion.
Actual Position
Desired Position
Joint Angles / Pressure Points
Figure 5. Block diagram of tethered water hammer design. Figure 4. Virtual robotic arm controlled by Artificial Neural Network trained to map the inverse kinematics.
B. Propulsion – Water Hammer The water hammer effect, also known as a fluid hammer, has been known since the introduction of the modern plumbing. Until recently it has been viewed as a negative effect with great capacity to destroy indoor and outdoor plumbing and, as a result, a number of remedies were developed to mitigate its effects. This phenomenon occurs when a fluid, customarily water, traveling through a pipe, experiences a rapid and sharp change in pressure usually facilitated by a fast closure of a valve. Resulting is an increase in pressure, at the point of the closure, brought about by the continuous motion of the flowing liquid. What follows is the expansion of the surroundings, a pipe or a hose, primarily in the direction of the fluid motion but also in a lateral direction, which is the cause of pipe shattering. The intensity of the effect is inversely proportional to the time in which the valve is closed: the shorter the shutoff time the greater the force of the effect. Recently Perrin et al. [39]-[40] demonstrated the feasibility of harnessing this potentially devastating effect towards a useful application. In an experiment a wheeled object was placed at the end of a tether which featured a looped hose with a shutoff valve, see Figure 5. The object was a remotely operated car with a small electric engine. The weight of the tether, due to its length, presented a difficulty for the car to pull. The main challenge of the task was when the tether was placed
B1. Active Tether Most robots can be divided into two broad categories: untethered and tethered. The most obvious advantage of untethered solution is the fact that the robot is unattached to any base station which allows for much more complex travel route. However, this freedom comes with a price. Communication is usually achieved via wireless pathway which is susceptible to signal interference and degradation and, as such, is often unreliable. Tethered approach eliminates this deficiency, additionally providing for almost limitless power and the ability of retrieval in case of catastrophic failure. These advantages are contrasted with three main drawbacks. First, every tether has a finite length. Second, very long tether may become heavier than the overall payload of the robot. Third, if the path of the robot is not straight but curvy, tether often becomes tangled around corners. What is needed is to use those disadvantages as advantages of the tethered approach. Water hammer seems like a perfect solution to those problems. Increasing the length of tether is no longer an issue as longer hose would provide more water increasing the water hammer effect. The restriction of tangled tether has also been deemed as defeated as was recorded by Perrin. The issue of limited range due to the finite length of tether still remains though. Because of the active nature of the water hammer effect, this solution has been coined an active tether in contrast to a passive tether, that is a tether unaided by the water hammer effect. B2.Directed Propulsion
Our team has conducted a number of experiments aiming at repeating and validating the water hammer effect. Both of the claims, namely forward propulsion and untangling capabilities have been validated. Figure 6 presents an experiment where a front-mounted vehicle is aided by an active tether, with a weight being placed on top of the tether. A vehicle with an active tether was able to pull a weight of almost double the amount of a passive tether (6.25 kg vs 3.85 kg) [52].
Tubing”, with inside diameter of 6.35 mm, outside diameter of 11.11 mm, and wall thickness of 2.38 mm, with the overall length of 7.62 m. The valve was a general purpose, 2-way, 24V DC solenoid valve with a weight of about 2 kg, operated at 8 Hz. The valve moved about 4 to 5 cm within 20 second interval with direction provided in Table 1.
Shape 1
Shape 2
Figure 7. Initial pipe shapes for directed propulsion due to water hammer effect experiment. Arrows indicate direction but not the magnitude of propulsion. Figure 6. Drag Test of Active Tether due to the water hammer effect.
Table 1 1 2 3 4 5 6 7 8 9 10 M ean
III. SYSTEM ARCHITECTURE
Sh ape 1 3 8.2 o
Sh ape 2 6 5.2 o
3 5.2 o 4 3.2 o
6 8.0 o 7 0.1 o
3 8.4 o 3 9.8 o
6 4.0 o 6 3.6 o
The all-polymer robot would employ an all-polymer computational center as means of replacing a CPU. Active tether would be used either as a sole source of propulsion or as a supplement to a primary drive, such as an electrical motor. However, SNN could also be used in conjunction with the water hammer effect, namely with the directionality of propulsion.
4 2.4 o 4 0.5 o
6 2.3 o 6 6.0 o
A. SNN and Directed Propulsion of Water Hammer
3 6.0 o 4 0.5 o
7 1.8 o 6 6.3 o
3 7.1 o 3 9.1 3 o
6 4.7 o 6 6.1 9 o
For an all-polymer robot, we need to eliminate the wheeled robot from Figure 5 so that only the tubing is actuating itself. (Feller et al. discuss ways of eliminating the valve in [40].) Our team has set out to investigate another hypothesis, namely that the shape of the active tether results in directed propulsion. The observation is that the shape of the hose impacts the direction of propulsion of the valve, which is consistent with a lumped, finite element model of the fluid in the hose. For a preliminary test of this hypothesis, the hose was arranged into two distinctively different shapes denoted as Shape 1 and Shape 2 in Figure 7. The experiment included two 5-gallon tanks with about 80 psi output, the pipes were “High-Pressure Clear PVC
Artificial Neural Networks are primarily used to extrapolate the relationship between the input and output. Provided with enough examples, they can determine the general mapping of the relationship. The strength comes from the fact that the precise mapping does not have to be known. As demonstrated in our experiment detailed in previous section, there is a relationship between the direction of the propulsion and the general shape of the hose. However, this relationship is not fully known and determining the exact formula would require extensive theoretical analysis as well as exhaustive practical experimentation. Therefore, it seems rather intuitive that employment of an ANN would be the most appropriate. Additionally, a permanent placement of SNN along the length of the hose would ensure that the relationship between shape and direction would be continuously evaluated. B. Additional Work
Though not a main focus of this paper, it should be mentioned that the architecture of a memory design has been demonstrated to be successfully employed in creation of polymer-based display device, namely organic light emitting diode array. The structure of this OLED is virtually identical to the structure for an organic photovoltaic (OPV) or solar cell. Because the motion power for this type of robot comes from a pump external to the system, the small power levels generated by OPVs are appropriate for control making the creation of all-polymer robot feasible with this approach. IV. CONCLUSION An all-polymer robot would greatly expand on the operational capabilities of today's search and rescue robots by removing the constraint of rigid bodies machines. Flexible machines not only can reach places that are currently off-the-limits for hard-cased robots but they also benefit from lower costs of manufacture and lower power consumption. Water hammer effect has been proven to successfully propel an object mounted at the end of a tether. The effect is also powerful to be employed in a situation where a tether is tangled underneath a door or locked around two round objects. These facts alone demonstrate that tethered rescue robots would benefit from the use of this phenomenon.
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As already demonstrated, there is a strong relationship between the directionality of propulsion due to water hammer and the shape of the hose. This effect could be harnessed either in aiding the steering or perhaps as a sole source of directionality of movement.
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