low cost robot

8/2/2019 Low Cost Robot

1/6

Autonomous Mobile Robot System Concept Based On PSO Path Planner and

vSLAM

Abdullah Zawawi MOHAMED

Department of Advanced Manufacturing andMechanical Engineering

University of South AustraliaSouth Australia, Australia

[email protected]

Sang Heon LEE, Hung Yao HSU

Department of Advanced Manufacturing andMechanical Engineering

University of South AustraliaSouth Australia, Australia

[email protected]

Abstract

The idea of placing small mobile robots to move around in alarge building for potential intruders detection has been

aroused for some time. However it is still facing two major

hurdles: not to lose its ground truth and to make a decision onhow to move around safely and effectively within reasonablecomputation cost. This paper describes a mathematical modelfor a development of a scheme for an autonomous low costmobile robot system using visual SLAM (simultaneouslocalisation and mapping) and Particle Swarm Intelligent pathplanner. The result showed that this system could provide a

solution for the problem in indoor mobile robot navigation.The advancements of the computer technology make thistechnique as a cost effective solution for a future home servicerobot.

Keywords-component; autonomous robot; vSLAM; PSO;path planning

I. INTRODUCTIONSuccess behind navigation of autonomous mobile robots

heavily relies on the four factors : perception, the ability ofthe robot to interpret data from its sensors to meaningful

data; localisation, the ability of the robot to determine itsposition in the environment; cognition, the ability of the

robot deciding on how to act to achieve its goals; and

motion control, the ability of the robot to modulate its motor

outputs to achieve the desired trajectory [1]. However the

noise from the sensors is propagated with the time and this

would lead to misunderstandings of its true location. In theearly stages of autonomous robot development, theresearchers used to combine data from laser scanner,

encoder, ultrasonic or infrared beacon and others as devices

to avoid obstacles and get precise positions. Another critical

factor for a successful navigation of a mobile robot is path

planning. Path planning research dated few decades evenbefore the research on mobile robots was born. However

still there is no explicit answer for the autonomous mobile

robots navigation problem. Many suggestions and attempts

have been made by researchers to solve the problem, forexample, visibility graph, fuzzy logic, ant colony

optimization (ACO), artificial potential field (APF) and

genetic algorithm (GA). For the implementation of suchalgorithm in real-time applications, there is however one

important issue that must be addressed; the computationcost. In GA [2] based path planning, the environment

around the robot usually coded from starting point to the

goal and the optimal path is achieved through continuousoptimization using the selection, crossover and mutation

operator, which is characterized by the global optimization

ability and robustness. However, this technique has

disadvantages: the large searching space, complicated

coding and high computation cost. ACO on the other hand

has better robustness and global optimization. However thesearching ability is limited to the division of the grid. ACO

is a good option to solve shortest path searching problems

when the paths are already created [3]. On the other hand,

APF is a simple model and has low computation cost. It ishowever easily trapped into local minima. The magnitude of

grid division also affects the path planning directly [4]. A

larger size of grid division will decrease the computation

cost, however the capability of path finding will be

weakened. Fuzzy logic has good capability to overcome the

local minimum, however incompleteness of experiences andthe fuzzy reasoning table will rapidly expand with the

increase of input quantity are the disadvantages of the fuzzylogic [5].

A thorough literature review conducted indicates that

Particle Swarm Optimisation (PSO) proposed by Eberhart in

1995 has gained a lot of attentions among researchers in

numerous fields especially in computer science for its

special attributes which are fast convergence to the global

optima and robustness [6]. This research is conducted to

investigate the performance of PSO to solve the global pathplanning problem for mobile robot. Many experiments need

to be done to get the best parameter settings to achieved

desirable results. One of the biggest challenges iscompromising between computational cost and shortest

path. A higher number of particles will cost more

computational cost however it will produce shorter path

than the lower number of particles. The path planning is

generated as the robot moves since vSLAM is assumed to

be implemented in this experiment.

___________________________________

978-1-4244-8728-8/11/$26.00 2011 IEEE


2/6

II. CONCEPTUAL ARCHITENTURE OF AUTONOMOUSMOBIEL ROBOT CONSIDERED

Conceptual architecture of the autonomous mobile robot

consist of path planning, localisation and motion control toadapt to the real world using the perceptual information

obtained from sensors. It is widely known that v-SLAM can

provide a solution for accurate positioning for indoor

environment [7] and is considered as a major breakthrough

for consumer applications [8]. In v-SLAM, a mobile robotautonomously explores the environment with its on board

sensors, gains knowledge about the environment, interprets

the scene, builds an appropriate map and localises itself

relative to the map.

Continuous landmarks creation approach is a novelattributes of this technique [9]. The landmark is described

by an ordered set of data, where Ii is the image of the

landmark and Si is the position estimate of the landmark. Amap, m, is consists of a set ofk landmarks. In the first run

the robot estimates the landmarks based on its odometry

data alone however when it revisits the landmarks it uses

information from known landmarks to estimate an improved

robot pose. This technique will minimize the positioning

error produced by the odometry drift. In any occasions when

the robot does not sense any known landmarks this system

will take two specific actions. The first action is the systemwill update robots pose estimation based on odometry

measurements relative to the last position estimation. In thiscase the odometry inputs from the encoders are fairly

accurate for a short moving distance however for a longer

travel distance it might introduce large errors due to

accumulative errors in input readings. When the error grows

larger and the system might accept an old landmark as a

new landmark due to the position errors. This will be

compensated when the system finds the reference landmarks

in system library. The landmarks position will be taken intoconsideration and the accurate re-adjustment of the robots

position according to it will be made by the system. Thissystem also helps the robot to locate itself when it is

manually moved to another position in the environment,

known as kidnapping, by the user. A problem with current

implementation of v-SLAM is the landmark database. The

number of landmarks grows as the size of area grows. One

of the approach to overcome this problem is the system

would not register two landmarks which are located tooclose to each other. Detail on the mathematical modeling for

this technique can be obtained from the recent research

papers [10, 11].

In this paper, v-Slam is implemented as follows.Localisation uncertainties generated from the odometry

changes when the robot moves to a new position is updated

in the Extended Kalman Filter (EKF) using odometry

update. Landmarks which meet the pre-filter algorithm are

then updated to the system memory when the robots moveto a new position. These landmarks are then compared with

landmarks library in the system using Speeded Up Robust

Features (SURF) object recognition technique [12].

III. PSO FORPATHPLANNINGParticle Swarm Optimisation (PSO) is a method to find

the optimal solution by imitating social behaviour of fish

schooling bird flocking [13]. The group of individualachieves the objective based on common information from

every member. L. Lu et al [14] have researched on the

potential of PSO for path planning in unknown

environment. Basic principle in their algorithm is an agent's

position which is nearer to the target will produce greaterfitness value, in contrast if the position of an agent nearer to

the obstacle will produce lower fitness value. They have

tested their algorithm with two robots moving in opposite

direction to the target with the existence of a moving

obstacle. Their robots arrived to the target without singlecollision. However in the initial setting they set the speed of

the mobile robot at a relatively slow speed in order to allow

the computer to optimize the path planning. Comparing tothe other systems this system only needs partial informationfrom the limited range sensors in order to navigate without

collision in dynamic environment.

PSO algorithm works by simultaneously maintaining

several particles or potential solutions in the search space.

For each iteration of the algorithm, each particle is

evaluated by the objective function being optimized, based

on the fitness of that solution.

The PSO algorithm is made in three steps, which only

will the when meet the exit condition. The pseudo code forthe canonical PSO shows below:

Step 1: Initialiase population with random positions and

velocities

Step 2: Evaluate-compute fitness of each particle in

swarm

Step 3: Do for each particle in swarm {

Step 3.1: Find particle best (pbest) - calculate

fitness value of each particle.Ifpbestin current iteration > previouspbest

Pbest= currentpbest

Endif

Step 3.2: Find global best (gbest) best fitness of

allpbest

Step 3.3: Update velocity of particle as per Eq. 1

Step 3.4: Update position of particle as per Eq. 2

Step 4 :} Repeat step 3.1 through 3.4 until terminationcondition is reached

( + 1) = () + 11[ () ()] + 22[ () ()]...... (1)( + 1) = () + ( + 1 ) (2)

Where parameters used are:= inertia coefficient( + 1 )= current particles velocity()= previous particles velocity( + 1)=current particles position


3/6

()= previous particles position1 = uniformly distributed random number [0:1]2

= uniformly distributed random number [0:1]

1 and 2= acceleration constants ()= current pbest()=previous particles position ()= current gbestA population of particles is created and then evaluated to

compute their fitness and find both the particle and global

best from the population of swarm. The particle best, pbest,

represents the best fitness value for a particle up to that

moment in time, whereas the global best, gbest, represents

the best particles fitness value in the swarm. Each particle

is accelerated toward its pbest and gbest locations usingacceleration values c1 and c2 at each time step. These

constants c1 and c2 decide the relative influence of the socialand cognition components, typically these constants are set

to the same value to give equal weight to each social and

cognition component. The search process is terminated

whengbestarrives at the target position.

In order to keep the particles from moving too far beyond

the search space, if the particles move too far away from the

search space there are two common problems will occur;

firstly the system will converge in fewer iterations howeverthe paths are not smooth and secondly the particles willmove outside the search space without converge. Velocityclamping is a technique to limit the maximum velocity of

each particle. For a search space bounded by the range

[-xmax, xmax], velocity clamping limits the velocity to the

range [-vmax, vmax], where = . The value ofcan be set by the user.Another important element in PSO is , an inertia factor

which is introduced by Eberhart and Shi [15] after they

found PSO searched wide area effectively, however tends to

lack local search precision. helps PSO to dynamically

adjusts the velocity over the time by gradually focusing thePSO into local search.

A.Fitness evaluation

1 =( )2

()2 (3)

2 = ( 1 1) , 0 , > (4) = 1 + 2 (5)Where; and = particles position atx andy coordinates1= Euclidean distance from the robots position to thetarget2 = Safety distance from the particle to the obstacles

F= Fitness function for the whole path planning

optimization problem

= radius of the circled obstacle

=particles distance to the centre point of the obstacleThe goal of PSO is to minimize the fitness function F.Therefore the path planning problem must be described as

fitness function, this path planning problem becomes an

optimization problem. The path planning problem in this

research divided into two smaller fitness function; First

fitness function f1 is to make the path to the target andsecond fitness function f2 is to avoid the obstacles. is the particle distance to the centre point of theobstacle. If the particle distance is larger than the radius of

the circle obstacle, the particle is in safe distance hence no

correction action needs to be taken. Fig. 1 shows the processof determining centre point of the obstacle by using image

processing technique. Detected obstacles area is wrapped

into rectangular box Fig 1. (a). to find the centre point anddiameter. Based on this centre point and diameter a circle is

created to simplify the obstacle avoidance system as well as

minimizing the risk of local optima trap.

(a) ( b )

Figure 1. (a) Wrapped obstacle in rectangular box.(b) Obstacle in circle

Figure 2. Particles obstacle avoidance algorithm

Where,

A : New coordinates

B : Particles position fall into obstacles area

C: Centre point of the circled obstacle

r: Radius of the circled obstacle

p: Distance from B to C

The particles obstacle avoidance is based on the equation

below.


4/6

= ()

(6)If any particle fall into the obstacles area and obstacleavoidance mechanism based on Eq. 6. is activated to find

the new position for the particle. The particle is forced out

perpendicular to the perimeter of the obstacle.

IV. SIMULATION SET UPThis PSO path planner performed in MATLAB with the

simulation environment as presented below.

x Fig. 3. shows the simulation space is 100 pixel by100 pixel rectangle plane with 3 obstacles.

Figure 3. The position of the obstacles in the

simulation study

x Only several parts of the obstacles body perceivedby the camera, simulating partial known

environment path planning.

x The safety distance from the robot to obstacles is 5pixels.

x The robot perceived the obstacles one after anothermeans the path planning will change depending on

the detection of the obstacles by the camera.

x Population size: In general, any evolutionarysearch algorithm show improved performance with

relatively larger population. However, very large

population size will take greater cost in terms offitness function evaluations without producing

significant improvements. In this simulation thepopulation size is set to 1000, 100 and 30.

Figure 4. Graphical representation of the gbestswarm (left) and lbest (right) neighbourhood

topologies

x The neighbourhood topology: In this simulationgbest swarm as shown in Fig. 4. is used as

neighbourhood topology, all particles areneighbour of each other; thus, the position of the

best overall particle in the swarm is used in the

social term of the velocity update equation. This is

based on the assumption that gbest swarms

converge fast because all the particles are attracted

simultaneously to the best position of the search

space. The swarm will converge slower in lbest

swarm form because only a specific number of

particles can affect the velocity of a given particle

[16].x Safety distance: The safety distance between the

robot and obstacles is set to 5 pixels.

x Cognitive and social coeffiecient c1 and c2: Both ofthese coefficients are set to 2.05 (full model) based

on the setting experiment from previous studies[17,

18]. However in cognitive and social coefficient

experiment these values are varied.

x Inertia is set to 1.3 as a benchmark.x vSLAM is assumed implemented in this

simulation.

V. RESULTSThree different situations simulated in this experiment. Thefirst experiment is the simulation for the path planning in

the known environment both complex and simple obstacle

avoidance. Second situation is the path planning with time

updated obstacles information. In this experiment thenumber of the obstacle will increase as the robot moves

toward the target. The results from final simulation show theeffect of cappingXmax.

Figure 5. Simulation results for the path planning in for

unknown environment.

Fig. 5 shows the simulation results of the unknown

environment. The robots will update the path planning based

on the information from the camera at the time t. In this

experiment the robots managed to find the path and avoidedthe obstacles appears in its vision system. Every time the

robot finds new obstacle which is not in its library it willstop and recalculate the path.


5/6

A.Xmax setting

(a)Xmax 100 , 3.6 secs (b)Xmax 50, 5 secs

Xmax 25, 7.9secs (d)Xmax 10, 17.1 secs

Figure 6. Paths created by system in several differentXmaxsetting

In addition to canonical PSO, to control the randomness

of the possible solution in this algorithm Xmax is

implemented. Xmax is the maximum distance that a particle

can move. As shown in Fig. 6. limiting the maximum

distance will help the particles find more set of solution thus

it will create a smoother path in comparison to non cappingalgorithm. On other hands this effort will slow down theconverging time by the system. The number of particles

either 100 or 10000 would not affect the computational cost

much however implementing Xmax will increase 5 times of

the non capping algorithm.

Figure 7. Invalid path in complex known environmentXmax =100

Based on the Fig. 7. when Xmax is set too high the

possibilities of the system to create an invalid path is higher

as shown in the black square. Xmax not just important to

smooth and optimized the path but it is also a major factor

to create valid path to the target.

B.Population sizePopulation size gives a big impact to the performance of

the path planner. Even though Shi and Eberhart [19]reported that PSO is not sensitive to the population size

however for the path planning the fact is contradict to their

belief. Carlisle [20] suggested a population size of 30 is a

good option, however he also indicated that it might be too

small to be efficient, yet large enough to produce reliableresult. Table 1 shows the results from the experiments of

various particles population. When the number of

population increased it resulted shorter distance however asmall amount of the computational cost increased. The

environment for this simulation is based on the Fig. 5 where

the target is (800, 900).

TABLE I. EFFECTS OF THE POPULATION SIZE TO THE PERFORMANCE

Population 30 100 500 1000

Distance 1327 1322 1315 1310

Time(sec) 8.7 9 11.8 14.9

C. Cognitive and Social RatioIn [21], Kennedy suggested that the sum of the values of

cognitive and social components of the PSO (C1 and C2)should be about 4.0 and it is common to set both of the

components 2.05 each. Carlisle [20] varied the setting of C1and C2 and found by setting a higher social component the

performance of the PSO increased. He set C1 to 1.3 and C2

to 2.8. Based on the Table 2, it is clear that a higher social

coefficient as suggested by Carlisle produced an optimal

distance in comparison to other settings.

TABLE II. EFFECTS OF THE COGNITIVE AND SOCIAL RATION TO THEPERFORMANCE

Inertia

Value

C1 and

C2=1.49

C1 and

C2=2.05

C1 and

C2=2.5

C1=1.3

C2=2.8

Distance 1320 1310 1307 1295

Time(sec) 15.2 15 14.6 14.6

D. Genetic Algorithm vs PSOVarious studies show that PSO algorithm can outperform

genetic algorithm and other conventional algorithms [22] forsolving many optimization problems. This is partially due tothe fact that the broadcasting ability of current best

estimates gives a better and quicker convergence towards

the optimality. Even though a GA is theoretically ergodic

which means an individual can reach any point in problem

space with one jump or one generation, it is no ergodic in a

practical sense because of the multiple steps required in

space with obstacles.

Fig. 8. shows a comparison between path created by GA

[23] on the left side and the result created by PSO on the


6/6

right side for the same initial position and target. PSO

created better path in comparison to GA for the same path

planning problem.

a) Genetic Algorithm b) PSOFigure 8. A comparison results between Genetic Algorithm

and PSO

VI. CONCLUSIONIn this paper, investigative results on using particle swarm

optimization to solve near optimal path problem have been

reported. A higher population PSO will increase the quality

of the results however it drawback is higher computationalcosts are needed. Based on the application either fast

decision of optimal path the number of particle can be

decided. Social ratio also plays important role to increase

the performance of the PSO. A higher value social ratioleads to better results in contrast with full model where the

cognitive and social ratios are the same value. The next step

in this research is to solve the optimization problem of

simultaneous localization and mapping by using particle

swarm optimization.The results are highly encouraging with much better

performance exhibited by the proposed particle swarmoptimization based path planner. It should be noted that the

performance of the PSO path planner can be improved

further if the best parameters combinations are found and

further investigations are underway for this purpose.

ACKNOWLEDGMENT

The first author would like to acknowledge support by

Ministry of Higher Education, Malaysia for the scholarshipgiven to carry out this research.

REFERENCES

[1] Rusu, R.B. ; Robotin, R. ; Lazea, G. ; Marcu, C., Towards Open

Architectures for Mobile Robots:Zeero, In Proceedings InternationalConference On Automation, Quality and Testing, Robotics 2006,

pp.260

[2] Gao. M, Xu. J, Wu. H, Path planning for mobile robot based on chaosgenetic algorithm, In Proceedings 4 th International Conference on

Natural Computation, ICNC 2008, pp. 409-413

[3] C. Yuwan, S. Choingzhi, X. Nanggang, W. Lu, Path planning methodfor mobile robot based on Ant Colony Optimisation algorithm,Electronics and Applications, 3

rdIEEE Conference on Electronics and

Applications, pp.298-301.[4] Ningning Qi; Bojun Ma; Xian'en Liu; Zhenxin Zhang; Dongchun Ren;,

A modified artificial potential field algorithm for mobile robot path

planning, In Proceedings of World Congress on Intelligent Control andAutomation 2008, pp.2603-2607.

[5] Prandhan, S.K., Parhi, D.R. and Panda, A.K., Fuzzu logic techniquesfor navigation of several mobile robots, Applied Soft Computing

Journal 9(1), pp.290-304.

[6] Lu, M., Wu, D.-P., Zhang, J.-P., A particle swarm optimization-basedapproach to tackling simulation optimization of stochastic, large-scale

and complex systems, Lecture Notes in Computer Science (includingsubseries Lecture Notes in Artificial Intelligence and Lecture Notes inBioinformatics) 3930 LNAI, 2006, pp 528-537.

[7] Munguia, R.; Grau, A.; Closing Loops With a Virtual Sensor Based onMonocular SLAM Instrumentation and Measurement, IEEETransactionson Volume 58, Issue 8, Aug. 2009, pp. 23772384. DOI:

10.1109/TIM.2009.2016377

[8] P. Pirjanian, N. Karlsson, L.Goncalves E. D. Bernardo, Low-Cost,Visual Localization and Mapping for Consumer Robotics,http://www.wv.inf.tu-dresden.de/~wiki/Robotics/Media/Paolo%20Pirjanian/vSLAM-Paper.pdf

[9] Krim, S.A., Elaziz, O.A.A., Chatila, R., A computationally efficientEKF-vSLAM, In Proceedings of 16th Mediterranean Conference on

Control and Automation, pp. 511-516.

[10] Sren Riisgaard,; Morten Rufus Blas,; SLAM for Dummies : A

tutorial approach to Simultaneous Localization and Mapping

http://ocw.mit.edu/courses/aeronautics-and-astronautics/16-412j-cognitive-robotics-spring-2005/projects/1aslam_blas_repo.pdf

[11] K. T. Song, L. D. Yuan, Mobile robot loop closing using monocularvision SLAM, In Proceedings of SICE Annual Conference 2010, pp.1920-1923

[12]Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool "SURF:Speeded Up Robust Features", Computer Vision and ImageUnderstanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008

[13]James Kennedy, Russell C. Eberhart, Yuhui Shi, The Particle Swarm,Swarm Intelligence, pp. 287-325

[14] Li Lu; Dunwei Gong;Robot Path Planning in Unknown Environments

Using Particle Swarm Optimization, Natural Computation, 2008. ICNC'08. Fourth International Conference onVolume 4, 18-20 Oct. 2008,

pp:422 - 426

[15] Shi, Y. H., Eberhart, R. C., (1998). Parameter Selection in Particle

Swarm Optimization, The 7th Annual Conference on EvolutionaryProgramming, San Diego, USA.

[16] J. Kennedy and R. Mendes; Population structure and particle swarm

performance, In Proceedings of the IEEE Congres on EvolutionaryComputation (CEC) 2002.

[17]K.E. Parsopoulos, M.N. Vrahatis, Parameter selection and adaptationin unified particle swarm optimization, Mathematical and ComputerModelling, Volume 46, Issues 1-2, pp. 198-213.

[18] Y.Shi and R.C. Eberhart, Parameter selection in particle swarmoptimization, In Proceedings of the Seventh Annual Conference on

Evolutionary Programming, pp. 591-600, 1998.

[19] Shi, Y. H., Eberhart, R. C., (1998), Empirical Study of Particle Swarm

Optimization, 1999 Congress on Evolutionary Computing, Vol. III, pp1945-1950.

[20] A. Carlisle, G. Dozier., An off-the-shelf PSO, Proceedings of the

Particle Swarm Optimization Workshop, pp. 1-6.[21] Kennedy, J. (1998). The Behavior of Particles, 7th Annual Conference

on Evolutionary Programming, San Diego, USA.

[22] Kennedy, J., Spears, W.M.: Matching algorithms to problems: anexperimental test of the particle swarm and some genetic algorithms onthe multimodal problem generator. In: Proceedings of the IEEE Intl

Conference on Evolutionary Computation, pp. 7883 (1998)

[23] Jianguo, W., Yilong, Z., Linlin, X., Adaptive Genetic Algorithm

Enhancements for Path Planning of Mobile Robots, Proceedings on

International Conference on Measuring Technology and MechatronicsAutomation 2010, pp. 416-419.

low cost robot

Documents