Path-finding Algorithm for Ground Multiple Sensor Nodes Detection of

Quad-rotor-typed UAV

Hanshang Li and Ling Wang Dept. of Computer Science and Technology

Harbin Institute of Technology Harbin, China

Shuo Pang and Massood Towhidnejad ECSSE Department, Daytona College of Engineering Embry-

Riddle Aeronautical University Daytona Beach, United States

Abstract—Quad-rotor-typed UAV can perform multiple targets detection effectively by its controllability and agility. In this study, the ground multiple targets detection with stochastic distances between the different targets is considered. A target position is described as a two-dimensional coordinate. The all positions’ coordinates and the start-point coordinate of UAV are the sources of the algorithm. After some calculating, the algorithm should direct the UAV fly over all targets with the shortest flight line. The algorithm produces an anticipated flight plan based on genetic algorithm, and considered the characteristics of quad- rotor. It has a high performance in environment with few interference factors and can be used in a quad-rotor-typed UAV without GPS or any other polarizing means.

Keywords-Quad-rotor; UAV; ground multiple targets detection;

path-finding;GA.

II. INTRODUCTION

Wireless sensor network (WSN) has become popular in a wide range of applications in which the sensors are usually deployed into a large area. The sensors must be directly or indirectly connect with an agent which will retrieve the sensing information in these networks. Because of some characteristics of the sensors, more and more people become interested in using a mobile agent to retrieve the sensing information. And unmanned aerial vehicles could be a good choice to be mobile agents. Therefore, it is necessary to find some path-finding methods for UAVs so that it can perform the task more efficiency.

Quad-rotors, also named quad-rotor helicopters, are commonly designed to be UAVs. They are a kind of aircrafts that are lifted and propelled by four rotors. Quad-rotors are classified as rotorcraft, as opposed to fixed-wing aircraft, because their lift is derived from four rotors. Unlike most helicopters, quad-rotors use fixed-pitch blades, whose rotor pitch does not vary as the blades rotate; control of vehicle motion is achieved by varying the relative speed of each rotor to change the thrust and torque produced by each. These vehicles use an electronic control system and electronic sensors to stabilize the aircraft. With their small size and agile maneuverability, these quad-rotors can be flown indoors as well as outdoors. Nowadays, quad-rotors are used to implement

more and more different jobs such as aerial photography, search and rescue, transporting and so.

This research is focused on the path-finding method to plan a shortest path for UAV base on the GA (genetic algorithm).In this research we assume that the UAV can be guided to detect the target autonomously or be pointed to the target by human operation using land station with an existing autopilot. The locations of all targets and UAV can be described in a frame of axes. The method is implemented in simulation system in this paper. Then it will be used in a real quad-rotor-typed UAV and improved based on performance in the future work.

III. OVERVIEW OF QUAD-ROTOR

A. Quad-rotor Structure Fig. 1 below is the schematic diagram of reaction torques

on each motor quad-rotor aircraft. Rely on spinning rotors, rotor 1 and rotor 3 (color blue) spin in one direction while rotor 2 and rotor 4 (color green) spin in the opposite direction, yield opposing torques for control. Recent generation of quad-rotors install an autopilot in the center of gravity (or close to gravity center) to control the attitude of quad-rotor.

Figure 1. Schematic of quad-rotor aircraft

2013 10th International Conference on Information Technology: New Generations

978-0-7695-4967-5/13 $26.00 © 2013 IEEE

DOI 10.1109/ITNG.2013.80

477

B. Quadrotor Attitude and Basic Action Analysis Generally, people choose the same rotors and symmetrical

propellers for a quad-rotor so that the aircraft can keep balance easily by autopilot. Related with average speed of all rotors keeping balance, each rotor has three states: speed keep, speed up and speed down (Fig. 2). All actions of quad-rotor can be completed by the three states of each rotor. In other words, people change the states of four rotors to control quad-rotor without any other physical changing. It is more simple and easy to implement for people who is lack of knowledge about flight vehicle design.

Figure 2. States of each rotor

The basic actions of a quad-rotor can be divided into two classed: actions keeping balance, and actions losing balance. For the first class (Fig. 3), it is easy to understand that all rotors keep the same speed. The speed changes, the trust changes. The quad-rotor can hover, climb and descend depending on the relationship between gravity and lift force.

Figure 3. The first class of actions

For the second class of actions, the balance will be broken. The balance may be torque force balance or thrust balance. Braking the balance will change the state of quad-rotor and support to implement actions. The basic actions include rotate (Fig. 4) and translation (Fig. 5).

Figure 4. Quad-rotor Rotate

Figure 5. Quad-rotor Translation

Quad-rotors are more agile than other types of aircrafts because the basic actions are more steerable. The aircraft can be divided into four similar parts. Each of them can be head or tail without rotating. And every complex action can be composed by some basic actions. It means the complex action (Fig. 6) can be planned with basic actions and implemented by performing the basic actions (Fig. 7) concurrently. View the Fig. 6 and Fig. 7, a soared away action is divided into climbing and translation. The attitude of quad-rotor should be slant like Fig. 5, and all rotors should give a bigger thrust to make it climbing. Other complex actions are also able to be implemented by this method such as swirling climbing, curved flight, swinging even somersaulting.

Figure 6. Plan view of a complex action

Figure 7. Plan view of action analysis

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C. Advantages of Quad-rotor Compared with other types of aircrafts, the advantages of

the current generation of quad-rotors are following:

First of all, quad-rotors do not require mechanical linkages to vary the rotor blade pitch angle as they spin. This difference simplifies the design and maintenance of the vehicle. The basic actions of quad-rotor can implement most complex actions by cooperation. This advantage makes quad-rotor become usable for person who is lack of knowledge about mechanics.

Secondly, quad-rotor is more agile than helicopter and

fixed-wing aircraft. The separated rotor of quad-rotor can be controlled singly, acceleration or deceleration. Then the attitude of quad-rotor can be adjusted easily and quickly. This characteristic make quad-rotor highly performance indoor.

Besides, the use of four rotors allows each individual rotor to have a smaller diameter than the equivalent helicopter rotor, which allowing them to possess less kinetic energy during flight. This solution reduces the damage caused should the rotors hit anything. For small-scale UAVs, this makes the vehicles safer for close interaction. Some small-scale quad- rotors have frames that enclose the rotors, permitting flights through more challenging environments, with lower risk of damaging the vehicle or its surroundings.

IV. FLIGHT PATH ALGORITHM

A. Problem Statement The aircraft must fly over all the sensors which have been

settled down in a certain area, and collect the messages from them. The problem that how the aircraft can finds the shortest path is very similar to TSP (Traveling Salesman Problem), so it can be modeled as an undirected weighted graph, such that sensors are the graph's vertices, paths are the graph's edges, and a path's distance is the edge's length. An aircraft tour becomes a Hamiltonian cycle if and only if every edge has the same distance. The model is usually a complete graph in which the ere is an edge between each pair of vertices. If no path exists between two cities, adding an arbitrarily long edge will complete the graph without affecting the optimal tour.

In brief, we need search for a range of an integer subset x={1,2,…n}, so that the formula

),(),(1

1 11 � −

= ++=n

i iind vvdvvdT

is minimized. The elements of that mentioned above are the serial number of the sensors, and ),( 1+ii vvd means the distance from the sensor iv to the sensor 1+iv .

Figure 8. Situations of craft cruising

It is easy to image that each target has a matching available-range in flight plane for a multiple targets system in situation H3 (Fig. 9). The further factors from real working will be considered in real-testing. They are some mechanical characteristics of quad-rotor, some environmental factors and others.

Grand Flight plane

Figure 9. Model for situation H3

Fig. 10 shows how targets and aircraft can be digitized into the model. All targets are similarly recorded in this model using serial number, location. Every target has been given a unique SN. The location is described as 2D coordinates. In the path-finding model which will be used by TSP, the craft’s start-point is similar with a target which will be also flight over. The distance between every two targets can be computed. (X ,

B. Model Building

SN: 0 (X , Y )

2 2

SN: 2

0 0

Fig. 8 shows the situations of craft cruising. Point O is a target which can be sensed in a range with the effective radius R. In the research, we assume the quad-rotor’s attitude is less than R, situation H2 and H3. And in order to find a general enable method, we just build model H3.

Start Point

(X 1, Y 1)

SN: 1

Distance ( O1 , O2 ) = (X1 - X2 )2 + ( Y1 - Y2)2

Figure 10. Model of TSP

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C. Prototypical Algorithm A GA (genetic algorithm) is a search heuristic that mimics

the process of natural evolution. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

While parallel searching in the whole space, Genetic

algorithm can focus on searching for high-performance parts so that it can improve the efficiency and easily fall into local minimum. Meanwhile, with an inherent parallelism, the algorithm has a large number model that can handle the population's genetic treatment, and it is easy to be achieved parallel.

The description about how GA solves the optimal combination problem is as follows (the ‘k’ is the serial number of the generation):

1) Determine the population size N (integer); use the random method to produce possible solutions )(kX i as the population of the initial solution.

2) Calculate the fitness f( )(kX i ) for each individual )(kX i .

3) Calculate the survival probably for each individual )(kX i , then set random method to produce the parent individuals by )(kX i .

4) Produce the next generation population. And then choose two parent individuals )(1 kX , )(2 kX to make two new generation ones )1(1 +kX , )1(2 +kX till N individuals come out by some combination rules such as crossover and mutation.

5) Repeat step2, step3 and step4 after the end condition are met.

In suitable conditions, the fitness will increase from

generation to generation and converge on a maximum. The main issues involved in encoding schemes, fitness design, constraint handling, selection mechanism and so on. In the practical application of combinatorial optimization problems, the appropriate structure of the genetic algorithm is the key of the genetic optimization problem.

D. GA for Path-finding Genetic Algorithms (GA) are stochastic optimization

methods using the concept of natural evolution and natural genetics. The standard GA has some shortcomings as premature convergence, low convergence speed and low robustness. Adaptation of parameters and operators is one of the most important and promising parts of the research in GA. So in this part we will present a method for optimal path- finding design of an improved adaptive Genetic algorithm.

1) Operating Parameters of GA When GA is running, parameters should depend on the

specific problems to be solved. Because there is no theory of parameters algorithms for all applications of GA so far, these parameters that are crossover rate, mutation rate and the population size need to refer to the actual situation.

2) Encoding and Fitness

In the GA of solving path-finding problems, it is very common to make an encode scheme by the order of spots. For example, 8-2-6-3-1-4-5-7 means that the aircraft fly from the starting spot 8 to the ending spot 7 by the order before returning to the spot 8. And this is the most natural encoding scheme. However, it makes difficulties in the process of crossover and mutation.

The other way of encoding is by the edge combination. For example, the ‘2’ in 2-4-5-3-6-8-7-1 represent the distance from the first spot to the second one. This encoding scheme also has the same defect as the above one.

The fitness reflects the quality of an individual, and it means the greater the fitness is, the better the quality of the solution is. As the driving force of GA, the fitness is the unique criterion of natural selection. Designing fitness should be combined with the requirements of solving the problem itself, so in the path-finding problem, the path length can become the fitness.

3) Crossover Based on the problem of sequence encoding, if a simple

cross-point or multi-point crossover strategy is adopted, it must generate an illegal path. To solve this problem, crossover and mutation should be revised appropriately to meet the restrain requirements. There are many crossover operators, such as partial matching crossover, order crossover, cycle crossover and heuristic crossover.

Take the heuristic crossover operator as an example. And the basic idea of three exchanging heuristic crossover is as follows.

A 3 2 1 4 8 7 6 5 B 2 4 6 8 1 3 5 7 C 8 7 5 6 4 3 2 1

The initial city is randomly selected, j=1(the position number) and =3(the spot number). Turn right and make the ‘3’ the first place.

A 3 2 1 4 8 7 6 5 B 3 5 7 2 4 6 8 1 C 3 2 1 8 7 5 6 4

As distance (3, 2)> distance (3, 5): A 5 2 1 4 8 7 6 B 5 7 2 4 6 8 1 C 5 6 4 2 1 8 7 The result is: O 3 5 7 6 8 4 2 1

4) Mutation

At present, there are a variety of mutation operators, such as the reversal mutation operator, heuristic mutation operator. And the reversal mutation operator is very common.

In individuals two reversal points are randomly selected, and then two reversal points of gene begin to exchange.

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Parameters Values

Number of sensors 25 Radius of the scope 170

Population size 50 Selected propotion 0.6

Maximum of generations 15 Probability of crossover 0.08

Probability of mutation 0.5

Before mutation

1346798205

After mutation

1246798305

5) GA and Adaptive GA(AGA) In the parameter of GA, the crossover rate and mutation

rate directly affect the convergence speed. The size of the crossover rate determines the new individual for the pace, the greater the crossover rate, the more easily the old individual patterns are destroyed and the faster the new individuals generated. The crossover rate that is too high may make excellent individual patterns destructed, but the crossover rate that is too small will slow down the creating speed of new individuals, which causes the prematurity and stagnation of the algorithm. Therefore, the mutation rate is a key factor to determine whether the algorithm can jump out from local optimal solution. If the mutation rate is too small, it will be difficult to generate a new model structure, while the mutation rate is too high, it will become a purely random search algorithm. Whether it can converge to the global optimal solution will no longer depend on the operations of the selection, crossover and mutation, but mainly depend on the population size and evolution of the algebra. If we use standard GA, for different levels of optimization problems, we have to repeat tests to adjust the crossover rate and mutation rate, which is very inconvenient and difficult to guarantee the best parameter values.

When individual fitness is lower than the average fitness of the contemporary population, we think that this individual will have a poor performance. If this individual is selected under the selection mechanism, a larger crossover and mutation rate will be used; When the individual fitness is close to the maximum fitness of the contemporary population, we think that this individual will have a good performance and try to retain these fine mode, even if this individual is selected under the selection mechanism, a lower crossover and mutation rate will be used. Early in evolution, AGA is not ideal, because in the evolution early groups, the better individual is almost in a state of stability, at this time the best individual may not be necessarily a global optimal solution, which is easy to make the possibility of a local convergence for the evolution increase. In the later stage of AGA evolution, because in the early stage it is difficult to escape from the local optimal solution, the algorithm will eventually fell into the local convergence.

V. SIMULATION RESULTS

In this project, we use a type of quad-rotor UAV to carry out experiments. The UAV has four motors, one electric tuner for each motor, two pairs of airscrews with different direction, and an autopilot used for attitude controlling. The parts of the UAV and their specifications are shown in Table 1:

TABLE I. PARTS AND SPECIFICATIONS OF THE UAV

Parts Specifications

Body frame X650 V4 V8

Screw propeller APC 1147

Electronic speed

controller Pentium-40A

Charger SKYRC IMAX B6AC+

Lithium battery Dualsky XP50003GT-S,5000mAh 3S1P

11.1v,45C/6C Motor 2.5kg / Sunnysky X2814KV1000

Flight control unit DJI-NAZA

Remote controller JR XG7 2.4G/DMSS

Current probe UNI-T UT203

Revolution detector DM6234P Laser and non-contact type

According to the chapter above, we can complete the genetic algorithm with a specific programming language. At the same time, according to the actual situation in the algorithm the coordinates of the aircraft position can be added. The tool we used to develop the project is Code Blocks 8.02 and the developing language is C language.

We use a number of groups of points to simulate our implement. Considered the performance of the types of Quad- rotor-typed UAV we use and the basic needs of the experiments, we choose the appropriate simulation parameters. The initial parameters of the experiments are shown in Table 2(the population and the Maximum of generations could be adjusted after numbers of experiments to achieve the effective optimal degree). There are some points with random location in each group.

TABLE II. SIMULATION PARAMETERS

Fig. 11 shows one of them in which we use the line sections to draw the flight path of the Quad-rotor by AGA. We could find the optimization results of our simulations. And we also could use this kind of figure to find differences between the traditional GA and the adaptive GA in an intuitive way. Fig.

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12 shows the average total path length of traditional GA and AGA. In this figure we could find the differences between the performance of the traditional GA and adaptive GA. We could find that when the amounts of the nodes are small, the performances of these two methods are very similar to each other. However, as the amounts of the nodes rises, the adaptive GA shows it has the shorter total path length than the traditional GA. It means that when the amounts of the nodes are big, the adaptive GA has better performance in finding the shortest length of flight path.

adaptive genetic algorithm has the features of learning itself, and this is very critical for unmanned aerial vehicles. Through our experiments, we find that it is appropriate to implement the GA on the flight path planning. On the basis of that, the adaptive GA which is improved from traditional GA has a better performance in path-finding. Meanwhile, the AGA is so adaptive that it can have a better combination with aircrafts. In future work we could focus on the kinds of methods of improvements of the GA, so that we could improve the performance of it and make it be more adaptive in the path- finding of various types of tasks.

ACKNOWLEDGMENT This paper is supported by “the Fundamental Research

Funds for the Central Universities” (Grant NO. HIT. NSRIF. 2012049).

Figure 11. Flight path of the Quad-rotor

Figure 12. Average length of total path

VI. CONCLUSION

In this paper, we discuss the path-finding algorithm for ground multiple sensor nodes detection. Through many analysis and experiments about this project, we have selected the adaptive genetic algorithm from a number of algorithms based on the actual situation, the main reason is that the

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