Remote Guidance of Untrained Turtles by Controlling Voluntary Instinct Behavior.pdf

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Remote Guidance of Untrained Turtles by Controlling Voluntary Instinct Behavior Serin Lee1., Cheol-Hu Kim2., Dae-Gun Kim2, Han-Guen Kim1, Phill-Seung Lee2*, Hyun Myung1* 1 Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea, 2 Division of Ocean Systems Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea Abstract Recently, several studies have been carried
  Remote Guidance of Untrained Turtles by ControllingVoluntary Instinct Behavior Serin Lee 1 . , Cheol-Hu Kim 2 . , Dae-Gun Kim 2 , Han-Guen Kim 1 , Phill-Seung Lee 2 * , Hyun Myung 1 * 1 Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea, 2 Division of OceanSystems Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea Abstract Recently, several studies have been carried out on the direct control of behavior in insects and other lower animals in orderto apply these behaviors to the performance of specialized tasks in an attempt to find more efficient means of carrying outthese tasks than artificial intelligence agents. While most of the current methods cause involuntary behavior in animals byelectronically stimulating the corresponding brain area or muscle, we show that, in turtles, it is also possible to controlcertain types of behavior, such as movement trajectory, by evoking an appropriate voluntary instinctive behavior. We havefound that causing a particular behavior, such as obstacle avoidance, by providing a specific visual stimulus results ineffective control of the turtle’s movement. We propose that this principle may be adapted and expanded into a generalframework to control any animal behavior as an alternative to robotic probes. Citation: Lee S, Kim C-H, Kim D-G, Kim H-G, Lee P-S, et al. (2013) Remote Guidance of Untrained Turtles by Controlling Voluntary Instinct Behavior. PLoS ONE 8(4):e61798. doi:10.1371/journal.pone.0061798 Editor: Alexandre J. Kabla, University of Cambridge, United Kingdom Received August 29, 2012; Accepted March 14, 2013; Published April 17, 2013 Copyright: ß 2013 Lee et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the srcinal author and source are credited. Funding: This research was supported by the IT Convergence Campus Fund of KAIST (G04100066), the Korea Ministry of Land, Transport, and Maritime Affairs(MLTM) as U-City Master and Doctor Course Grant Program, and a grant from Human Resources Development (No. 20114030200040) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Korean Ministry of Knowledge Economy. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.* E-mail: (PSL); (HM) . These authors contributed equally to this work. Introduction Several artificial intelligent agents, such as micro- and nano-aerial vehicles (MAVs/NAVs), have been developed for theperformance of tasks which humans cannot easily handle.However, these agents have not performed as well as expecteddue to the limitations of size/weight, battery capability/charging,range of operation, and so on. A major lesson, thus far, is that weare still far from artificially reproducing a level of intelligence evenof insects. Thus, interest in alternative approaches based onbiologically inspired or biomimetic methods has increased.Recent work on the direct control of lower animal behavior hasfocused on the measurement of operating range and speed, versuspayload and maneuverability, and on studies of animal socialbehavior [1]. Several mechanical control systems have beenreported, such as the insect flight control system proposed by Satoet al., which electronically stimulates the insect’s brain and musclesin charge of its flight [2]; the wireless communication device of Britt et al., which provides commands to a well-trained dog [3];the remote flight control system of Tsang et al., which uses micro-fabricated flexible neuroprosthetic probes integrated with carbonnanotube-gold nano-composites in a moth [4]; and the 2.5-mWwireless insect flight controller designed by Daly et al., whichutilizes a non-coherent pulsed ultra-wideband receiver system-on-chip (3–5 GHz) [5]. In studies of differential brain stimulation,Talwar et al. have shown that rats are easily guided by specificstimulation of either the somatosensory cortical (SI) or medialforebrain bundle (MFB) as a cue or reward, respectively [6]. Mostproposed behavior control systems require a well-trained animalor cause involuntary behavior by direct stimulation of thecorresponding musculature by an implanted controller. In insects,implantation is carried out at the adult or pupal stage.Our study has addressed the problem of control in twofundamentally different aspects: whether we can control anuntrained animal in a non-invasive and remote manner, and if this may be done via control of voluntary behavior. Our resultsindicate this is indeed the case. All animals, including humans,usually act by reaction to stimuli. In particular, a reactive behaviorconnected with bodily protection is essential and must occurquickly, and it must be evoked, mediated, and directed in aconsistent manner by a stimulus [7,8]. From these studies inturtles, we have observed a consistent pattern of control of ananimal’s movement trajectory utilizing the innate instinctivebehavior of obstacle avoidance, and we propose this as a novelbehavior control scheme. Using this non-invasive scheme, oursystem of animal behavior control can be more stable andadoptable. The system is suitable for application in taskstraditionally carried out by mobile robots, such as surveillanceand reconnaissance, exploration and navigation, as well as othermissions dangerous for humans.We first conducted experiments to investigate in detail theturtle’s obstacle avoidance behavior, in which we took advantageof earlier work on the turtle’s vision wavelength discrimination [9]and the observation that hatchling sea turtles recognize a whitelight source as an open space and so move toward it [10,11]. PLOS ONE | 1 April 2013 | Volume 8 | Issue 4 | e61798  Materials and Methods Turtles We decided to do our experiment with turtles because it is easyto detect their movement, and they are capable of living in varioustypes of habitats on land and in water. The turtles used in thisstudy were red-eared sliders (  Trachemys scripta elegans   ). Four turtleswere grown indoors in laboratories at the Korea AdvancedInstitute of Science and Technology (KAIST). The turtles werehoused together in a large, water-filled glass tub (91 6 61 6 20 cm).The tank was fitted with a water filter and a dry platform forbasking, and the turtles were sunbathed 6 , 7 hours under a UVlamp. They were fed commercial pellets four times a week. After atleast 6 hours without feeding in the tank, they were moved to thefloor of the laboratory or the experimental table for experiments asshown in Figure 1. As each experiment was repeated, the turtlesbecame sluggish from fatigue; therefore, different turtles were usedfor our experiments every 10 minutes. Thus, we carried out theexperiments using all four turtles (Figure 1). Method  As mentioned in the Introduction, this study aimed to controlturtle’s behavior by providing visual stimuli. We thereforeexamined how turtles respond to various visual stimuli. Theexperiments were performed in arenas on the experiment table(90 6 20 cm) (Figure 2) and the floor (223.8 6 166 cm) (Figure 3).The turtles’ responses, that is, their navigational paths, werecontinuously recorded by a simple color-based tracker. Except forour target stimulation, other factors (olfactory stimuli, auditorystimuli, room temperature, brightness distribution, etc.) werecontrolled during the experiments.Each turtle’s path was tracked by a 20 Hz digital camera(VLUU NV4, SAMSUNG, KOREA) with 800 6 592 pixelresolution. The center of a circular color patch (radius=3 cm) Figure 1. Depiction of experimental remote-controlled visual stimulus delivery and tracking systems. (A) To examine the turtle’s visualobstacle recognition, an experimental arena was equipped with a camera and two movable cylinders as obstacles (shown from the side view andfrom above). The dimensions of the arena, surrounding walls, and obstacles are indicated. (B) Experiments performed on the laboratory floor area(with the dimensions indicated) are shown in the drawing. The placements of the turtle, obstacle, and tracking system are shown. (C) The embeddedcontrol system to block the turtle’s view is shown in the drawing. The servo motor controls the positioning of the semi-cylinder obstacle (in theimage, it is positioned directly in front of the turtle). The red circle on the controller tracked by the simple tracking algorithm was regarded as thelocation of the turtle. (D) The turtle was remotely controlled to follow the desired path by alternating the visual angle of the obstacle between 6 180(no stimulus) and 6 90 degrees (Movie S1).doi:10.1371/journal.pone.0061798.g001Controlling the Turtle’s Walking PathPLOS ONE | 2 April 2013 | Volume 8 | Issue 4 | e61798  attached to the center of the turtle’s upper shell was tracked by acolor-based tracker that used a MATLAB (The Mathworks Inc.,USA) image processing program developed by Maptic (See The average distance between thecenter of the patch and the end of the turtle’s head was 6 cm.The raw data from the color-based tracker was post-processedby projective transformation mapping of the oblique view to thetop view, and a Kalman filter with linear models was used for boththe dynamics of the system and the observation process. Like otherBayesian-based tracking algorithms [12], the parameters of thefilter were carefully chosen by an iterated trial-and-errorprocedure comparing the filtered and real trajectories by eye,and we found that we could obtain good results under thecovariances of  Q   = 10 { 3 and R  =0.1. The whole system isdescribed in Figure 1. Apparatus To provide the turtle with stimulus causing obstacle avoidance,a simple control device was designed to locate a semi-cylinder atany given angle with respect to the anteroposterior axis of theturtle. An embedded control module (5.3 6 7.5 6 4.8 cm, 133.5 g)was mounted on the turtle’s upper shell with the circular colorpatch for tracking, and a black semi-cylinder was used to block theturtle’s view. A micro controller unit (ARM Cortex-M3,STM32F101V8T6) received an angular value to control the servomotor (Maximum output angle: 2,160 degrees, Resolution: 4.9degrees, Motorbank, KOREA), which could rotate the black semi- Figure 2. Control of obstacle avoidance behavior. (A) The movement trajectories were tracked after turtles (red circles) were initially placed50 cm in front of obstacles. Obstacles were movable black or white cylinders (radius=5 cm, height=10 cm), and the turtles could push them to gopast. (B) Turtles were initially located 55 cm (marked by red circles at mid-carapace) in front of a movable black obstacle wall in an arena with whiteside walls.doi:10.1371/journal.pone.0061798.g002Controlling the Turtle’s Walking PathPLOS ONE | 3 April 2013 | Volume 8 | Issue 4 | e61798  Figure 3. Relationship between a turtle’s movements and visual angle to the obstacle. (A) Each movement trajectory was translated toplace the location at which the stimulus was given at the srcin, and then rotated so that its tangential line at that location coincides with the y-axis.The red lines represent the trajectories after the visual stimuli were provided. The black lines describe the trajectories before the stimuli. The angle of the obstacle is indicated numerically and by the image of the semi-cylinder. Two black dotted lines show orientation and comparison (n=10–21). (B)The radii of curvature (RoC) of the red trajectories in (A) are plotted by mean and standard deviation, although they were not always normallydistributed. (C) The average turning velocities (ATV) of the trajectories are plotted and analyzed as described for the RoC in (B). (D) To measure theturning behavior, a turning distance vector was defined as shown in this figure (see text for details).doi:10.1371/journal.pone.0061798.g003Controlling the Turtle’s Walking PathPLOS ONE | 4 April 2013 | Volume 8 | Issue 4 | e61798
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