Gerard Ely U. Faelden

Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm

There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization of a quadrotor without the use of cameras and GPS modules by means of transceivers and Genetic Algorithm (GA). This paper uses the received signals from the transceivers as inputs for the genetic algorithm in order to locate the quadrotor in a xyz axis. Parameters such as location of transceivers, amount of transceivers and population size of the GA are tested in order to determine a successful way of locating the quadrotor. Results show that the different parameters tested were successful and converges to a point usually with a fitness measure greater than 99%. An average fitness measure greater than 99.9900% served as a benchmark for the tests done. The first test achieved this benchmark at about 130 generations and the second test achieved it at 110 generations. The time it took for the program to locate the quadrotor is about 60 milliseconds. Results show that this blind localization technique is successfully locates the quadrotor and may be calibrated to ones own need.

Optimization of decentralized information dissemination in quadrotor swarm using genetic algorithm

There is a glaring problem in communication systems when it comes to a decentralized robotic swarm. Since a decentralized swarm would limit the awareness of each agent to its immediate surroundings/neighbors, the exchange of information between agents may now prove to be challenging. An epidemic-based broadcasting technique is then presented to resolve the problem of end-to-end agent communication. This paper aims to optimize the information diffusion by means of implementing genetic algorithm to optimize the time it will take for each quadrotor individual to acquire the information coming from a single source (i.e. the quadrotor who first received the information from an external stimulus). The method by which this is done is epidemic in nature. Due to this, for each time there would be a signal broadcasting, the genetic algorithm would be run to determine the next ideal location of each individual. A genetic algorithm was looped several times to achieve the desired solution. The results showed that for each run of the GA, the number of quadrotors having received the information continually increased until the output converges to a fitness level. However this only worked under certain constraints that need to be weighed out properly. This includes the readjustment of the fitness and crossover functions. Also, the parameters of the GA must be well calibrated for proper output response.

A genetic algorithm approach to swarm centroid tracking in quadrotor unmanned aerial vehicles

One of the trademark behaviors of a swarm is aggregation. Aggregation is the ability to gather swarm members around a specific point in space. The goal is to keep an object, stationary or moving, at the center of the swarm. This paper presents a novel approach to centroid tracking in robotic swarms. Genetic algorithm is used in quadrotor unmanned aerial vehicles to keep the object being tracked at the center while minimizing two parameters: the distance travelled by each quadrotor and the distance of each quadrotor from the object. Centroid tracking was found to have an average error of 0.0623568 units for swarm populations ranging from 10 to 100 with the lower swarm populations exhibiting lower errors. Convergence did not exceed the maximum of 23 milliseconds for populations less than 30. These results show that the algorithm is well-suited for implementation in swarms with lower numbers of quadrotors.

A neural network approach to a cooperative balancing problem in quadrotor-unmanned aerial vehicles (QUAVs)

There is growing interest in unmanned aerial vehicles (UAVs) such as quadrotors over the past several years. Cooperation among multiple quadrotors is one of the areas of focus. This paper proposes a neural network form of control for a cooperative task done by four quadrotors and will be tested through simulations. The task at hand is a ball and plate balancing problem during flight of multiple quadrotors carrying the plate. The objective is to maintain the keep the ball at the center of the plate even if the ball is introduced at different parts of the plate. The neural network controller will output the appropriate motor speeds of the rotors based on the detected area of introduction of the ball. Results show that the artificial neural network controller successfully directs the ball towards the center of the plate. The network outputs an average deviation of 0.00924 units from the expected PWM signal strength which corresponds to a 0.249% error from the expected value.

Obstacle avoidance for quadrotor swarm using artificial neural network self-organizing map

Swarm operation in Unmanned Aerial Vehicles is an emerging technology which has numerous uses. It can be used in industrial, agricultural, and even military applications. However, it must be able to perform formations for it to be effective. Also, countermeasures must be made by the swarm to account for certain obstructions that are present in the environment. This paper aims to address this issue by implementing an artificial neural network self-organizing map to give the correct coordinates to each swarm individual such that the swarm formation would be present in the given space while avoiding the obstructions present. Testing would include subjecting the system to three different obstruction patterns in a given 3D space. The results showed that for all cases, the swarm was able to avoid all the obstructions.

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.

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.

Implementation of an artificial neural network in recognizing in-flight quadrotor images

This paper shows an implementation of a feedforward artificial neural network capable of recognizing images of the CrazyFlie 2.0 quadrotor during flight. The network is to be used in a real-time quadrotor swarming application and has to be able to successfully differentiate pictures that show a quadrotor in flight versus pictures that do not. The network was trained using a standard backpropagation algorithm and images taken from a video of the said quadrotor in flight. These images were divided into three groups: a training set and validation set for the training stage, and a testing set for verification of the trained neural network. The results showed that the neural network was able to correctly identify the images in the testing phase 100 percent of the time while achieving a 94 percent accuracy for the images in the testing set.