Ryan Rhay P. Vicerra

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

This paper presents the fusion of swarm behavior inmulti robotic system specifically the quadrotors un-manned aerial vehicle (QUAV) operations. This studydirected on using robot swarms because of its key fea-ture of decentralized processing amongst its member.This characteristicleadstoadvantagesofrobotopera-tionsbecauseanindividual robotfailurewill notaffectthe group performance. The algorithm emulating theanimal or insect swarm behaviors is presented in thispaper and implemented into anartificial roboticagent(QUAV) in computer simulations. The simulation re-sults concluded that for increasing number of QUAVthe aggregation accuracy increases with an accuracyof 90.62%. The experiment for foraging revealed thatthe number of QUAV does not affect the accuracy ofthe swarm instead the iterations needed are greatlyimproved with an averageof 160.53iterations from 50to 500 QUAV. For swarm tracking, the average accu-racy is 89.23%. The accuracy of the swarm forma-tion is 84.65%. These results clearly defined that theswarmsystemis accurateenoughto performthe tasksand robust in any QUAV number.Keywords: swarm robotics, swarm intelligence, socialbehaviors, unmannedaerial vehicles

Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform

This paper proposes a vehicle plate optical character recognition method using scale invariant feature transform integrated with image segmentation and fuzzy logic. Image segmentation separates every character in a plate area to get the features of every character obtained. Scale Invariant Feature Transform or SIFT on the other hand, allows the extraction of every feature of each character obtained from the plate. Fuzzy logic analyzes the features obtained from the SIFT algorithm which is proposed to detect the characters correctly. This program used MATLAB to determine the performance of the algorithm. Using the proposed algorithm, it was shown how the algorithm was effective on extracting plate character features as well as recognizing the characters in a given image. Results show that the algorithm has an accuracy of 90.75% and now ready to use for other implementation. This can be incorporated to present optical character recognition system and test its validity and accuracy for practical purposes.

Adaptive aggregation algorithm for target enclosure implemented in quadrotor unmanned aerial vehicle (QUAV) swarm

This paper presents aggregation behavior algorithm that will be applied for unmanned aerial vehicle quadrotors (QUAV). The most basic behavior for natural swarms is aggregation. Other swarm or social behaviors are derived from the aggregation behavior. Due to the concept of independence, each swarm members are required to collect themselves together to perform a certain task. However the swarm faces different environments thus this behavior is very complex to accomplish. This is the reason why the researchers developed this paper for multi robotic systems. Simulations were done to test the said algorithm and the researchers garnered the accuracy of 90.85%.

A neural network model for a 5-thruster unmanned underwater vehicle

Unmanned underwater vehicles (UUVs) are mostly used for safe underwater explorations and researches. UUVs are subject to different parameters that changes over time. Such parameters are not considered in kinematic modelling of vehicles. As such, a dynamic modelling of underwater vehicles is necessary. This study proposes a dynamic model that is utilizing Artificial Neural Network (ANN), for a 5-thruster underwater vehicle design. The training data for the ANN model is gathered by empirical methods. The dynamic model is represented by UUV variables: thrusters input voltages and resulting velocity vector. The results of the neural network showed accuracy and reliability due to the low Mean Square Error (MSE) and satisfactory regression plots.

Formation stabilization algorithm for swarm tracking in unmanned aerial vehicle (UAV) quadrotors

This paper presents swarm formation algorithm for swarm tracking behavior in multi robotic system of flying quadrotor unmanned aerial vehicles (QUAV). Multi robotic system ensures the success of the task through the increase in members of the swarm. This characteristic is very suitable for tracking moving objects. Another key feature would be the decentralized processing of the swarm. The loss of a swarm member would not contribute significantly to the swarm. The behaviors were patterned to biological traits of insects and animals and are applied to computer applications. Simulations were performed and results showed that swarm tracking accuracy yielded 89.23%. This result is due to the accuracy of 84.65% of the formation behavior of the swarm. Furthermore, the aggregation behavior further contributed with an accuracy of 90.62%.

Implementation of varied particle container for Smoothed Particle Hydrodynamics - based aggregation for unmanned aerial vehicle quadrotor swarm

The property of the Smoothed Particle Hydrodynamics (SPH) method of being mesh free, adaptable and suitable for tracking of individual particles makes it appropriate for approximating swarm behaviors for multi-agent robotics applications. The researchers modeled each of the swarm robots as SPH particles and then subjected them to external forces to exhibit aggregation and force certain formations. The external forces subjected to the SPH particles are gravity forces and container constraints . The containers explored in the study are simple geometrical primitives: sphere and cube . Computer simulations were done to show how SPH can facilitate in forcing swarm formations with the help of bounding primitives. Algorithm benchmarking was done to show how well SPH performs. Results show that SPH performs better than the benchmark algorithm by a margin of 0.703 and 1.016 units for the two set-ups, respectively. Actual robot implementation was also done to verify the effectivity and viability of the proposed algorithm in exhibiting the aggregation behavior. After 15 seconds of system run time, the interparticle distance and motion accuracy reached 96.93% and 91.14%, respectively.

Path planning of underwater swarm robots using genetic algorithm

Path planning is one of the most exciting challenges in building autonomous swarm robots. It consists on finding a route from the origin of the robot to its target destination. It becomes more difficult when some obstacles are added to the environment. This paper consists of multiple obstacles: the robots and their possible path. This paper will present the path planning of underwater swarm robot based on genetic algorithm. Swarm robots will determine the position of pre-defined object and genetic algorithm generates shortest path for each robot to reach the object without collision to one another. The xyz coordinates of possible path of robot are randomly generated and they are encoded into chromosome and their fitness is defined by the summation of their displacement using Euclidian distance formula for 3-dimensional plane. The simulation results demonstrated that proposed algorithm is able to plan safe collision free paths for swarm robots. It also shown that using more population, the optimum path will be obtained. The implementation of genetic algorithm is done using computer simulation and explains the process in section two of this paper.

Swarm Robot System for Underwater Communication Network

This paper presents a swarm robot simulator for im-plementing underwater wireless communication net-work. Swarm intelligence is based on the collectivebehavior of social insects and animals such as ants,bees and others. In this paper, swarm was appliedto overcome the challenges of transmitting data in alarge underwater environment. A robot considered tobe a member of the swarm acts as a simple “physical”carrier of the data, it moves until they converge andmanageto formalink connectingthe data transmitterand receiver. The system is developed, simulated andtested using a coded simulator.Keywords: swarm intelligence, underwater communica-tion simulation, underwater swarm robot system, swarmrobotics

Formation control in quadrotor swarm aggregation using Smoothed Particle Hydrodynamics

This paper uses the Smoothed Particle Hydrodynamics technique to perform formation control of quadrotor swarms. The swarm is to be modelled to behave like water. A simple aggregation behavior is exhibited with certain primitives that act as obstacles to force formations from the swarm. Different primitives are implemented to manifest various formations. Results show that SPH outperforms APF by a margin of 7.31% for a cubic container primitive and by a margin of 27.81% for a spherical target enclosure primitive. Formation control was successfully implemented using Smoothed Particle Hydrodynamics and is proven to be more efficient than the benchmark algorithm.

Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model

Swarm robotics is one of the novel approaches being explored in multiple quadrotor. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm behavior aggregation in a quadrotor swarm. It is implemented over a quadrotor swarm testbed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model with a linear attraction and bounded repulsion. Results show successful demonstration of the aggregation algorithm with minimal error in position. It is tested for an increasing number of quadrotors and errors are seen to increase with swarm size. Results show an error of 3.293 cm from the individual target position for aggregation. It also shows and average aggregation speed of 1.896 secs for all test while having an increase in aggregation speed of about 1.772 sec per increase in swarm size. The time in aggregate is seen to be at an average of 98.5405% of the time. All the tests show successful demonstration of the swarming behavior which can now mark the start of development of implementation of more complex swarming behaviors.