Swarming Algorithm for Unmanned Aerial Vehicle Quadrotors Paper: Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm 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 robot swarms because of its key fea- Mobile robots 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 insect 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 robotics. 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: swarm robotics, swarm intelligence, 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 insects [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. Vol.18 No.5, 2014 Journal of Advanced Computational Intelligence 745 and Intelligent Informatics Bandala,A.A.etal. 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.