Algorithm for Unmanned Aerial Vehicle Quadrotors

Paper: Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Behavior for Aggregation, Foraging, Formation, and Tracking –

Argel A. Bandala, Elmer P. Dadios, Ryan Rhay P. Vicerra, and Laurence A. Gan Lim

De La Salle University, Manila 2401 Taft Avenue, Manila 1004, Philippines E-mail: {argel.bandala@dlsu., elmer.dadios@dlsu., [email protected]., laurence.ganlim@dlsu.}edu.ph [Received February 5, 2014; accepted May 3, 2014]

This paper presents the fusion of swarm behavior in quite different when combining or creating conventional multi robotic system specifically the quadrotors un- robotic systems [2, 5]. There are a handful of researches manned aerial vehicle (QUAV) operations. This study that attempted to achieve swarm robotic principles [3, 4]. directed on using swarms because of its key fea- Mobile can be dispersed in remote and unknown ture of decentralized processing amongst its member. areas which later can organize their formation to assist This characteristic leads to advantages of robot opera- humans [6–9]. tions because an individual robot failure will not affect This paper exhibits the compatibility of applying the group performance. The algorithm emulating the swarm algorithm on UAV quadrotors for aerial surveil- animal or swarm behaviors is presented in this lance, search and reconnaissance operations through flight paper and implemented into an artificial robotic agent formations and reconfiguration by abiding swarming pat- (QUAV) in computer simulations. The simulation re- terns and behavior. The endeavor to achieve this is enu- sults concluded that for increasing number of QUAV merated in this paper as follows. Section 1 introduced the aggregation accuracy increases with an accuracy the current trends and researchers that involve swarm in- of 90.62%. The experiment for foraging revealed that telligence and . Section 2 enumerates the swarm the number of QUAV does not affect the accuracy of behaviors that this paper tackles and how does these be- the swarm instead the iterations needed are greatly haviors differ from one another. Section 3 relates swarm improved with an average of 160.53 iterations from 50 behavior into artificial systems specifically QUAVs and to 500 QUAV. For swarm tracking, the average accu- presents methodologies to incorporate these behavior to racy is 89.23%. The accuracy of the swarm forma- robots. Section 4 discusses the appearance, design and tion is 84.65%. These results clearly defined that the algorithms in the simulation environment. Section 5 swarm system is accurate enough to perform the tasks presents the results of the experiment done for every be- and robust in any QUAV number. havior. Section 6 presents the interpretation of the data from Section 5 and suggests some recommendation for future works. Keywords: , , social behaviors, unmanned aerial vehicles 2. Swarm Behaviors and Motivation of Swarm Intelligence 1. Introduction Generally all existing works related to swarm intelli- The field of robotics specifically mobile robotics is di- gence were derived from the group or social behavior of rected towards the use of multiple robots in accomplishing animals or [1, 2]. These animals are naturally dis- tasks [1, 2]. The nature of these algorithms enables the de- organized and solely depends on the actions that other signer to create simple robots which is generally cheaper individual exhibits. Based on local communication with and less complex compared to a single robot [1, 2]. The nearby swarm members and the actions that they do, an algorithms used to test the intelligence of the multiple individual can evaluate and later generate an appropriate robotic systems are customarily from the idea of using behavior that will contribute to the group’s objective. All or mimicking animal behavior [1–3]. The algorithms of the members of the swarm are considered as data har- that utilize this, is collectively referred to as swarm al- vester from the environment, thus any ongoing activity of gorithms [1, 4]. an individual can immediately influenced by the environ- The fusion of swarm algorithm into robotic systems is ment.

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2.1. Aggregation may track a target and continuously reconfigure its for- The most basic of all the swarming behavior is aggre- mation to avoid the tracked target which further enables gation [3]. Aggregation is the ability of the swarm to the swarm to avoid obstacles along the trajectory [3, 10]. work together and gather or organize itself in one specific location and avoiding collision with other swarm mem- bers [1, 3]. Thus, Aggregation is one behavior that can- 3. Robot Swarm Algorithms and its Implemen- not be neglected in any robotic system. This behavior tation to QUAV establishes the ability of each individual to communicate or observe the action of one another since swarm mem- The robot swarm of QUAVs are considered to be in bers are in close distance with one another. Normally it the area of interest which operates in the bounded in n is combined with other swarming behavior to accomplish [3, 11]. The elements of the swarm is represented by goals [6, 9–11]. xi(t),wherei = 1,2,3,...,N, this representation repre- sents the status or state of every robot with quantity N t 2.2. Social Foraging with respect to time . The controlling function for every individual robot is represented by the function ui(t),and Another biological swarm behavior that is greatly for uniformity i = 1,2,3,...,N [3]. adopted in swarm robotics is foraging [3, 10, 12]. This behavior is a manifestation of aggregation. It is the col- 3.1. Aggregation lective reaction of the individuals base on the environ- The swarm with elements N robots with the follow- ment, thus the environment is a great factor in the be- ing conditions mentioned above, is ideally desired to con- havior as well as the swarm can greatly alter the envi- verge in a singular point or within the vicinity of a singular ronment [1, 13]. Here, the swarm is concerned primarily point until steady state t = ∞ is given by: in to two regions of the environment. These regions are lim  xi(t) − x j(t) ≤ ε, ...... (1) the favorable regions and the danger regions [1, 3, 10, 13]. t→∞ Similarly the regions are analogous to food and predator regions, the favorable region is the area where food for where ε is the measure of the maximum swarm size. The the swarm is abundant while dangerous region is repre- functions xi(t) and x j(t) are input parameters in which sented by a region where a prey of the swarm is present i = 1,2,3,...,N and j = 1,2,3,...,N as defined in [3]. and needs to be avoided. 3.2. Social Foraging n → 2.3. Flight Formation Foraging can be described by mapping at any point i ∈n where σ(i) are points that can have three Flight formation is a manifestation of foraging process ranges of values. Generally σ(i) > 0, σ(i) < 0, or σ(i)= which is greatly described by creation of geometric con- 0 are the possible values for these points [3, 10]. These figuration of initially unorganized individuals [3, 14]. The points can represent the regions of interest in the area. convergence to an organized configuration is greatly chal- σ(i) is used to represent a point or an area with great in- lenged by the constraints of the movement of each indi- terest, or a point of avoidance or risk, or a neutral point vidual while maintaining the geometric configuration re- in the space. σ(i) > 0 is used to define an area in which quired by the scenario. Biological swarms usually utilize the swarm should avoid and will generate a higher proba- this ability for various reasons, the formation generated bility that a robot will not traverse towards this point. On by a group of birds are their means to avoid predator and the other hand σ(i) < 0 is a point of great interest and catching a prey. The most unique for these animals are most likely that the swarm of robots will traverse in this they use flight formation to conserve or lessen the energy direction. Likewise if σ(i)=0 a neutral point is present. or effort that an individual exert in flying. In the formation Therefore a favorable area of point in the space will be they use, the leader serves as the absorbent of the force so represented by these values. The more favorable the area that other members of the swarm do not experience the is the lesser its value and the area of less interest should same amount of force [2, 5, 15, 16]. have a greater value; lastly a neutral point should have a value of 0 [3]. 2.4. Swarm Tracking One manifestation of flight formation and aggregation 3.3. Flight Formation is swarm tracking [2, 3]. There are instances wherein a A manifestation of combining aggregation and forag- target, either a prey or predator simultaneously move and ing is flight formation. Mainly to create a stable flight interacts with the swarm. The said target may have the formation, the distances between swarm elements should objective to capture the swarm or evade the swarm [3]. be maintained while moving. Given the desired dis- In this case a special kind of behavior is employed; the tances, dij|i, j ∈{1,2,3,...,N},i = j flight formation swarm needs to maintain a formation while tracking the is achieved [3]. Steady state can be described by [3]:   target. This formation may be used to enclose a tar-   lim  xT (t) − x j(t) −dij ≤ ε, get while flying, similar to aiding the target in travelling t→∞ from one point to another [1, 3]. In contrast a swarm ∀i = j ∈{1,2,3,...,N}...... (2)

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Fig. 2. Aggregation behavior in simulation.

Fig. 1. The environment of the simulator.

3.4. Swarm Tracking Formation creation can further be put into use by a be- havior called tracking. For swarm robotics mainly it is the translation of the swarm’s centroid from one point to another while maintaining the original formation, further- more the object being tracked can be set as the centroid of the swarm at any given time t. Fig. 3. Creation of formation in simulation. N x(t)= 1 x (t) ¯ N ∑ i ...... (3) i=1

The simulation initially generates the number of elements 4. Simulation Environment indicated in the settings and place them at random posi- tions. After which the robots will start to converge on the The methodology mentioned in the previous part was indicated centroid coordinates. The center of the swarm taken into account and by the use of it, a simulation en- is denoted by a red circle. This centroid indicator changes vironment is designed. Shown in Fig. 1 is the graphical real time as the robots move. Each element or robot con- user interface of the simulation program. As can be seen tributes to the position of the centroid. The swarm will swarm behavior can be selected by the dropdown input. stabilize once the centroid of the swarm converges into Included also, is the control for the number of swarm el- the desired point. ements to be included in the simulation. Lastly the coor- Figure 3 shows the output of the simulator when the dinates of the centroid as well as the control to alter it is setting is set to formation. The total count of robots can provided in the simulator using a textbox. The simulation be determined in the settings of the simulator. At the ini- can be initiated by the button simulate. The simulator can tial stage of the simulation, the robots were dispersed at run all of the four swarm behaviors. A log is generated to random points. After several runs the mobile robots con- record the movement of each swarm elements for analysis verge into distinct positions that resemble certain pattern and interpretation. or formation. The formation can be achieved by creation The simulator utilized a simple graphic representation of multiple centroid locations and dividing the number of of a UAVquadrotors. The dynamics and characteristics of robots into clusters and assigning the said centroids. the graphic representation abide the movement and fea- The remaining swarm QUAVbehaviors are time depen- tures of the real QUAV. Shown in Fig. 1 are white ele- dent thus tables of numeric description showing these be- ments which represent the QUAV. In addition to, the red haviors are provided in the next section. Note that for circle is drawn in the center represents the centroid or a social foraging, the simulator provides different sectors point that represent the whole swarm. The centroid can in the environment which indicates whether the area is dictate the point or area for aggregation. By moving this dangerous or favorable. Formations are formed with the point foraging will emerge because by continuously mov- swarm’s collective decision while in flight. Mainly the ing the centroid all of QUAVs will move and directly can swarm abides the rules and restrictions in foraging and search an area. Swarm tracking can be achieved also by aggregation. The tracking behavior of the swarm displays placing the centroid of the swarm on a moving target, thus the aggregation which allows the swarm to be in the vicin- will make the swarm continuously move with the target. ity of the target. Flight formation is observed because Figure 2 shows a sample display of the simulator after the tracked object is enclosed that resulted to formation it converged to its final state when it is in the aggregation development. Foraging is also observed and a very high mode. As can be seen, the number of mobile robots are priority is given to the coordinates of the target, thus the set into 100 and the centroid of the swarm is set to loca- centroid of the swarm is ideally found in the coordinates tion P(100,150). This is the same setting use in Fig. 1. of the tracked object.

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Table 1. The summary of aggregation experiment. Table 2. The summary of foraging experiment.

Number of Average error % Accuracy Number of Number of Number of Numbers swarm elements of centroid swarm iterations in failures of success (Robots) elements achieving 50 0.135 86.5 (robots) success 100 0.116 88.4 50 404.2 9.9 90.1 150 0.109 89.1 100 307.8 0.3 99.7 200 0.092 90.8 150 250.2 8.7 91.3 250 0.090 91.0 200 156.8 8.0 92.0 300 0.086 91.4 250 109.6 9.1 90.9 350 0.081 91.9 300 90.7 7.9 92.1 400 0.079 92.1 350 85.4 8.3 91.7 450 0.076 92.4 400 80.3 8.4 91.6 500 0.074 92.6 450 64.5 7.8 92.2 500 55.8 8.2 91.8

Fig. 4. Aggregation centroid accuracy.

Fig. 5. Speed of swarm foraging to desired point. 5. Experiment Results

The researchers evaluated the algorithm and simulation by experimentation. The simulation was operated in nu- merous trials and the results of the observations are pre- sented below. The mean of every trial are summarized in a table based on the behavior under test. The results are as follows: Table 1 presents the result for aggregation testing of the swarm. 100 trials were done for every robot count. The desired centroid of the user was analyzed and compared with the centroid produced by the swarm. As can be seen, the average error of the swarm is 9.38% and the average Fig. 6. Success rate of finding a desired point. accuracy is 90.62%. Error was calculated by finding the distance of the actual centroid with the desired centroid. Moreover Fig. 4 illustrates the characteristics shown in Table 1. It can be derived that when the number of robots average time for every swarm members. It is shown that in the swarm, the centroid placement would be more ac- there are times that a swarm element may approach the curate with the desired location. undesired area with an average of 8.66%. Table 2 presents the result of the foraging behavior of Figure 6 illustrates the behavior of the swarm in forag- the swarm 100 trials were done for every robot count, sim- ing. The success of the swarm to find the target ranges ilar to the previous experiment. Numerous point contain- from the high of 99.7% to 90.1% with an average of ing the region of interest and region to avoid were placed 92.34%. Further the said ranges are almost constant thus in the environment. The number of first touches for non- the number of swarm member is exempted in claiming the neutral region and the average iteration number of these credit for the increase or decrease in accuracy. touches was recorded Table 3 is similar to the first table the centroid error and As can be seen in Fig. 5 that, the iteration number be- accuracy is shown. The experiment is done by assigning fore hitting a desired point decreases while the number of a centroid position equal to the position of the object to robots in the swarm increases. Table 1 deducts that the be tracked. Similar to the first experiment there are 100

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Table 3. The summary of swarm tracking experiment. Table 4. The summary of swarm formation experiment.

Number of Average error % Accuracy Number of Average error of % Accuracy swarm elements of centroid swarm elements robot positions (robots) (robots) 50 0.195 80.5 50 0.131 86.90 100 0.153 84.7 100 0.137 86.30 150 0.112 88.8 150 0.140 86.00 200 0.105 89.5 200 0.144 85.60 250 0.098 90.2 250 0.148 85.20 300 0.093 90.7 300 0.150 85.00 350 0.088 91.2 350 0.155 84.50 400 0.082 91.8 400 0.162 83.80 450 0.077 92.3 450 0.183 81.70 500 0.074 92.6 500 0.185 81.50

Fig. 7. Tracking centroid accuracy. Fig. 8. Accuracy of swarm element’s position in formation.

trials and the object moves randomly in the environment. The average error of the swarm’s centroid is 10.77% and The aggregation behavior yields an accuracy of the average accuracy is 89.23% 90.62%.in placing the swarm centroid in a desired posi- In addition to, Fig. 7 can best describe the aforemen- tion. It can be concluded that the increase in the num- tioned results. The increase in robots which tracks the ber of robots can yield a higher accuracy. This directly target, the more accurate the tracking is. implies that the centroid can be controlled accurately by Table 4 indicates the summary of the experiment in increasing the number of robots because the resolution swarm formation. The swarm is directed a certain pattern of the swarm increases proportional with the number of or formation to configure. The robots formed the said con- swarm members. figuration. The difference in coordinates were recorded The foraging behavior experiment revealed that the and analyzed. As can be seen the average error in posi- time it takes to hit or reach a desired position decreases tion of the swarm is 15.35% and the average accuracy is with the increase in swarm members. This directly de- 84.65%. notes that the area covered by the swarm increases while Figure 8 shows the characteristic of the formation be- the number of members increases thus it is easier for the havior of the swarm QUAV. It can be generalized that in- swarm to search and reach the desired area. The accuracy creasing the number of swarm members will greatly af- of finding the desired point is not related to the number of fect the accuracy of the formation configuration. This can swarm members, this is can be derived in the experiments be further derived into the knowledge that every swarm in foraging. The main reason for this is that the swarm member is prone to create at least a deviation to the de- elements abide a rule of finding a target and avoiding the sired position. danger area. Therefore the only parameter dependent to the number of elements when it comes to foraging is the time the swarm will find the favorable point. 6. Conclusion Swarm tracking behavior is very similar to the aggre- gation centroid positioning behavior. The only difference This paper discussed and enumerated several swarming is that the centroid of the tracking behavior is placed on QUAV behaviors. The translation of these algorithms to a moving target. Similarly the resolution of the swarm QUAV is presented by demonstration in a simulated envi- is defined by the number of robots in the swarm QUAV. ronment. Increasing the robots will increase the resolution thus cen-

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troid adjustment is easier for swarm with less elements. [20] P. Doherty, et al., “A Distributed Architecture for Autonomous Un- manned Aerial Vehicle Experimentation,” Distributed Autonomous In formation configuration it is clear that the accuracy Robotic Systems, Vol.6, pp. 233-242, 2007. of the swarm’s position is greatly affected by the number of swarm elements. This is because increasing the num- ber of elements will increase the number of position error Name: contributed in the swarm. Primarily the main reason for Argel A. Bandala this is that each robot will always commit gradual mis- takes in looking for its new final formation position. Affiliation: Assistant Professor, Electronics and Commu- For future works, it will be good to include and in- nications Engineering Department, Gokongwei tegrate the characteristics of quadrotors in three dimen- College of Engineering, De La Salle University, sional space. An implementation of aggregation can be Manila implemented including other robot types i.e. underwater robots or land based robots. Optimized searching algo- Address: rithms in three dimensional domains can be derived from 2854 F. Manalo St. Punta, Sta. Ana, Manila, Philippines the foraging behavior. Lastly all of these behavior can Brief Biographical History: be mixed with other algorithms such as pheromone algo- 2008- Graduated Bachelor of Science, Polytechnic University of the Philippines rithm to introduce multi-tasking for the swarm. 2012- Joined De La Salle University as full time faculty 2012- Started Ph.D., De La Salle University 2012 Graduated Master of Science, De La Salle University References: Main Works: [1] I. Navarro and F. Matia, “An Introuction to Swarm Robotics,” ISRN • Development and Design of with IP-based Vision System. Robotics, pp. 1-10, 2013. • Swarming Algorithm for Unmanned Aerial Vehicle (UAV) [2] E. Sahin, “Swarm Robotics: From Sources of Inspiration to Do- Quadrotors–Swarm Behavior for Aggregation, Foraging, Formation and mains of Application,” Swarm Robotics: Sab 2004 Int. Workshop, Tracking. pp. 10-20, 2004. Membership in Academic Societies: [3] V. Gazi and K. M. Passino, “Swarm Coordination and Control Prob- • Secretary, The Institute of Electrical and Electronics Engineers (IEEE), lems,” Swarm Stability and Optimization, Chapter 2, pp. 15-25, Springer, 2011. Philippine Section • Secretary, Computational Intelligence Society Philippine Chapter [4] G. Beni, “Order by Disordered Action in Swarms,” Swarm • Robotics: Sab 2004 Int. Workshop, pp. 153-171, 2004. Robotics and Automation Society IEEE [5] A. F. T. Winfield, C. J. Harper, and J. Nembrini, “Towards Depend- able Swarms and a New Discipline of Swarm Engineering,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 126-142, 2004. [6] D. Payton, R. Estkowski, and M. Howard, “Pheromone Robotics Name: and the Logic of Virtual Pheromones,” Swarm Robotics: Sab 2004 Elmer P. Dadios Int. Workshop, pp. 45-57, 2004. [7] J. A. Rothermich, M. I. Ecemis, and P. Gaudiano, “Distributed Lo- calization and Mapping with a Robotic Swarm,” Swarm Robotics: Affiliation: Sab 2004 Int. Workshop, pp. 58-69, 2004. University Fellow and Professor, De La Salle [8] S. Lacroix and G. L. Besnerais, “Issues in Cooperative Air/Ground University, Manila Robotic Systems,” Roboics Research, Vol.14, pp. 421-432, President, NEURONEMECH, INC. Springer, 2010. [9] T. Balch, “Communication, Diversity and Learning: Cornerstones of Swarm Behavior,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 21-30, 2004. [10] R. R. McCune and G. R. Madey, “Swarm Control of UAVs for Address: Cooperative Hunting with DDDAS,” Procedia Computer Science, Miguel 106, DLSU, 2401 Taft Avenue, Manila 1004, Philippines Vol.18, pp. 2537-2544, 2013. Brief Biographical History: [11] O. Soysal and E. Sahin, “A Macroscopic Model for Self-organized 1996 Received the Doctor of Philosophy at Loughborough University Aggregation in Swarm Robotic Systems,” Swarm Robotics, pp. 27- 1997- Exchange Scientist, Japan Society for the Promotion of Science, 42, 2006. Tokyo Institute of Technology [12] Y. Altshuler, A. Bruckstein, and I. Wagner, “Cooperative Cleaners: 1998-1999 Director, Engineering Graduate School, De La Salle University A Study in Robotics,” The Int. J. of Robotics Research, Vol.27, 2003-2004 Director, School of Engineering, De La Salle University No.1, pp. 127-151, 2008. 2003, 2005, 2007, 2009, 2011, 2013 General Chair of HNICEM [13] H. Hamann and H. Worn, “An Analytical and Spacial Model of For- aging in a Swarm Robots,” Swarm Robotics, pp. 43-55, 2006. Main Works: • “Fuzzy Logic – Controls, Concepts, Theories and Applications,” ISBN [14] N. Ayanian, V. Kumar, and D. Koditschek, “Synthesis of Con- trollers to Create, Maintain, and Reconfigure Robot Formations 978-953-51-0396-7. with Communication Constraints,” Robotics Research Springer • “Fuzzy Logic – Algorithms, Techniques and Implementations,” ISBN Tracts in Advance Robotics, Vol.70, pp. 625-642, 2011. 978-953-51-0393-6. [15] R. D. Nardi and O. Holland, “UltraSwarm: A Further Step Towards • “Fuzzy Logic – Emerging Technologies and Applications,” ISBN a of Miniature Helicopters,” Swarm Robotics, pp. 116-128, 978-953-51-0337-0. 2006. • Research interests includes; Robotics, Mechatronics, Automation, [16] I. Maza and A. Ollero, “Multiple UAV Cooperative Searching Op- Intelligent Systems, Neural Networks, Fuzzy Logic, Genetic Algorithms, eration using Polygon Area Decomposition and Efficient Cover- Evolutionary Computation and IT. age Algorithms,” Distributed Autonomous Robotic Systems, Vol.6, pp. 221-230, 2007. Membership in Academic Societies: • The Institute of Electrical and Electronics Engineers (IEEE), Senior [17] W. M. Spears, D. F. Spears, R. Heil, W. Kerr, and S. Hettiarachchi, “An Overview of Physicomimetics,” Swarm Robotics: Sab 2004 Member Int. Workshop, pp. 84-97, 2004. • Founder and Chair of the IEEE Computational Intelligence Society, [18] K. Lerman, A. Martinoli, and A. Galstyan, “A Review of Proba- Philippines bilistic Macroscopic Models for Swarm Robotic Systems,” Swarm • IEEE Region 10 Executive Committee Robotics: Sab 2004 Int. Workshop, pp. 143-152, 2004. • Founder and President of the Mechatronics and Robotics Society of the [19] G. Beni, “From Swarm Intelligence to Swarm Robotics,” Swarm Philippines Robotics: Sab 2004 Int. Workshop, pp. 1-9, 2004.

750 Journal of Advanced Computational Intelligence Vol.18 No.5, 2014 and Intelligent Informatics Swarming Algorithm for Unmanned Aerial Vehicle Quadrotors

Name: Ryan Rhay P. Vicerra

Affiliation: Assistant Professor, Electronics Engineering De- partment, University of Santo Tomas, Manila Ph.D. Student in Electronics and Communi- cations Engineering, De La Salle University, Manila

Address: 279 Pastor St. Balut Tondo Manila, Philippines Brief Biographical History: 2000 Graduated B.S. degree course in Electronics and Communications Engineering, University of Santo Tomas 2001- Joined University of Santo Tomas as full-time faculty member 2008 Graduated M.S. degree course in Electronics and Communications Engineering, De La Salle University 2011- Started taking up Ph.D. in Electronics and Communications Engineering program at De La Salle University as a scholar of the Department of Science and Technology, Engineering Research and Development for Technology Main Works: • Swarm Intelligence for Underwater Swarm Robot System • Development of an Underwater Swarm Robot System • A Neural Network Model for a 5-thruster Unmanned Underwater Vehicle • Multiple Level Fuzzy Logic based Intelligence for Multi Agent Cooperative Robot Platform Membership in Academic Societies: • The Institute of Electrical and Electronics Engineers (IEEE) • IEEE Computational Intelligence Society • IEEE Philippines Section • IEEE Computational Intelligence Society Philippines Chapter

Name: Laurence A. Gan Lim

Affiliation: Associate Professor, Mechanical Engineering Department, De La Salle University, Manila

Address: 2401 Taft Ave., Manila 1004, Philippines Brief Biographical History: 1995- Faculty member at De La Salle University-Manila 2012 Obtained Ph.D. in Computer Science at Coventry University Main Works: • “Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images,” Proc. of 2012 IEEE Region 10 Conf. (TENCON 2012), pp. 1-5, 2012. Membership in Academic Societies: • Philippine Society of Mechanical Engineers (PSME) • The Institute of Electrical and Electronics Engineers (IEEE), Philippines Section Chair

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