Nowadays, building an intelligent robot that able to move by itself from one location to another without collides with other obstacles is of interest in many applications. In the real world, condition of an environment is always unpredictable and changes with the existence of dynamic obstacles. This paper tends to propose an algorithm for robot path planning in a dynamic environment using Genetic algorithm (GA) technique. The proposed algorithm is able to find an optimum path for a robot and avoid any static and dynamic obstacles. The variation of the proposed algorithm is shown with the implementation of the algorithm in 4-way movement robot and 8-way movement robot. The simulation results show significant performance of the algorithm when compared with real optimum path

Ibrahim, Mohd Faisal

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    Genetic Algorithm-based Robot Path Planning     Mohd Faisal Ibrahim, Adilah Zaira Abu Bakar and Aini Hussain Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MALAYSIA Email: faisal@vlsi.eng.ukm.my    Abstract  Nowadays,  building  an  intelligent  robot  that  able  to move  by  itself  from  one  location  to  another  without collides  with  other  obstacles  is  of  interest  in  many applications.  In  the  real  world,  condition  of  an environment is always unpredictable and changes with the existence of dynamic obstacles. This paper tends to propose  an  algorithm  for  robot  path  planning  in  a dynamic  environment  using  Genetic  algorithm  (GA) technique.  The  proposed  algorithm  is  able  to  find  an optimum  path  for  a  robot  and  avoid  any  static  and dynamic  obstacles.  The  variation  of  the  proposed algorithm  is  shown  with  the  implementation  of  the algorithm  in  4-way  movement  robot  and  8-way movement robot. The simulation results show significant performance of the algorithm when compared with real optimum path.    1. Introduction Path  planning  mechanism  has  been  applied  into many  latest  applications  that  required  automatic  path control such as UAV (Unmanned Vehicle Aerial), UGV (Unmanned Ground Vehicle), rescue robots, rocket and missile.  This  intelligent-like  mechanism  basically  has the typical aim to move an object from one position to another  determined  position  via  the  possible  shortest path without collides with any obstacles. In this paper, a Genetic algorithm (GA)-based path planning algorithm is  introduced  for  small-scaled  robot  movement  in dynamic environment. The algorithm considers method of handling static obstacles and dynamic obstacles.   One  of  the  important  elements  of  the  robot  path planning  is  the  ability  to  response  to  the  changes  of environment. Initially, the robot path is pre-determined based on static obstacles occurred in any possible path. However,  when  robot  moves  after  certain  moment, surrounding environment may change due to existence of  dynamic  obstacles  in  the  pre-determined  path.  The mechanism should be able to immediately identify new path  before  robot collides with dynamic obstacles that suddenly obstruct the current path.    This paper is organized as follow: the next section will discuss on parameters used for determining robot’s path.  Then,  implementations  of  GA  in  path  planning and program development are presented. The simulation result and comparison between two different parameters used are discussed and finally followed by future work and conclusion.    2. Robot Path Parameters Selection  of  path  parameters  is  the  first  task  that should  be  taken  in  path  planning.  Examples  of  path parameter  are  movement  angle,  movement  distance, movement direction etc. These parameters become the guidance for action of a robot. In [3], 3D movement is used as path parameter. Although the final result of the work has shown good performance but there are many mathematical equations used which are time consuming and impractical for real-time application.  In  this  paper,  two  types  of  path  parameter  are chosen  because  of  its  simplicity  in  calculation  but maintains  reliable.  The  first  parameter  is  4-way movement  and  the  second  parameter  is  8-way movement. As in figure 1(a), the 4-way movement is a combination of four determined direction which is north (0), south (2), west (3) and east (1). Meanwhile, the 8-way movement is an addition of 4-way movement with another 4 directions as in figure 1(b).  Figure 1. (a) 4-way movement (b) 8-way movement (a)  (b) Optimum path can be measured by using distance and time taken to move a robot from an initial location to a final location. Distance can be calculated by using simple mathematical equation or Pythagoras’s theorem. Time  taken  can  be  reduced  by  finding  path  with  the smallest changes of direction of movement. In this case, both  distance  and  time  will  be  used  as  measurement parameters to automatically find the possible optimum path of a robot. GA will use both parameters to identify optimum  path  as  will  be  described  in  details  in  the following section.     3. Identification of optimum path using GA   Many  algorithms  have  been  proposed  to  develop effective mechanism for automatic robot path planning [1][3][5].  However,  GA  has  shown  significance performance  compared  to  other  algorithms  in  term  of reliability.    GA is a class of stochastic search algorithms based on biological evolution [4]. It does not require derivative information;  it  can  provide  a  list  of  optimum parameters, not just a single solution [2]. Considering these  facts,  robot  path  optimization  can  be  performed effectively  with  GA  because  the  algorithm performs a parallel,  but  directed  search  to  evolve  the  most  fitted solution in a systematic approach.   3.1. Chromosome representation   Each  gene  in  a  chromosome  is  encoded  with suitable parameter to allow GA to search the best path solution.  Direct  encoding  is  used  to  ensure  simplicity and  reliability  in term of fast processing approach. In this  paper,  each  chromosome  of  GA  represents  a collection  of  next  direction  of  movement  in  every location point starting from initial point to end point as shown in figure 2 below:-  Dinitial_point  Dnext_point  Dnext_point  ……  Dfinal_point-1  where,   D = integer represents direction to next point.   Figure 2. Chromosome representation    Information in each gene can be used to identify the next  coordinate  or  location  that  a  robot  should  move. Number of genes in a chromosome is determined based on the minimum steps taken to reach the final point.  3.2. Cost function   Before  ‘selection’,  ‘crossover’  and  ‘mutation’ processes can be executed in GA, cost function which determines the target of GA search must be decided. As stated before, path distance and time taken to move a robot from an initial location to a final location can be used  as  the cost function in GA. Equation 1 below is used to calculate the cost function:-                (1) where,   w1, w2, w3: constants   D: differential distance   O:  a  number  of  path  coordinates  matches  with obstacles coordinates.    C: a number of changes of direction    Chromosome with the lowest cost function is said to have the best solution. Differential distance will be used to  identify  the  difference  between  final  location produced  by  each  chromosome  and  the  desired  final location. Each chromosome will be read to get the final coordinate of each possible path. If the final coordinate matches the desired destination of robot, the differential distance value will be zero which in turn will produce lower cost function value.    On the other hand, time taken can be reduced by counting number of changes of direction in the whole path. It is assumed that, time will increase proportional with the number of changes of direction.    This cost function also has direct relation to identify whether a suggested path collides with obstacles or not. Each location point in a suggested path will be checked with obstacle position. The cost function will increase drastically if any of the point matches obstacle position.   Therefore,  the  best  solution  is  said  to  have  the lowest  differential  distance,  the  lowest  number  of changes of direction and the lowest number of collision with obstacles. Figure 3 shows how the cost function is used to converge possible paths to the best solution.   Once the encoding of the chromosomes and the cost function  had  been  determined,  the  algorithm  of  an automatic  robot  path  planning  can  be  developed  as follows: 1. Define string with necessary length to represent parameter of whole path. 2. Make random initial population. 3. Evaluate individual fitness for every chromosome using cost function. 4. Perform  selection,  crossover  and  mutation operation  with  binary  string  representation  to produce new population. 5. Repeat step 3 and 4 until certain generation (set by user) to get the best chromosome. 6. Using the best chromosome, the best path in term of distance and time to reach destination will be obtained.                                      Figure 3. Cost function usage  4. Program development for robot path planning   In  general,  the  robot  path planning program holds two  main  components.  Both  components  involve  GA execution  to  find  optimum  path  with  determined parameters.  The  first  component  caters  path  planning with  static  obstacles  consideration  which  is  always executed  prior  to  robot  movement.  The  second component  involves  path  re-planning  with  dynamic obstacles consideration. The second component is only performed when robot has moved and there is obstacle suddenly  appears  on  the  pre-determined  path.  In  this paper, LabView software has been used to develop the program. The following sub-sections will further discuss both of the components of the program. 4.1 Path planning with static obstacles consideration   Cost function plays an important part in obtaining the  optimum  path  that  can  avoid  robot  from  collides with static obstacles. For the cost function calculation, it will start with initial coordinate at 0,0 assuming as the starting  position  of  robot.  Whenever  a  gene  value (represents  next  direction  of  movement)  is  obtained, current  coordinate  will  be  changed  according  to respective gene’s direction. The updated coordinate will then  be  compared  with  static  obstacles’  coordinate.  If the  coordinate  matches  with  any  static  obstacles’ position,  the  cost  function  for  the  path  formed  by particular  chromosome  will  be  doubled  to  a  large number to reduce the probability of the chromosome to be selected for next generation. By using this strategy, ‘unfitted’  chromosome  can  be eliminated very quickly and make convergence process faster.    4.2 Path planning with dynamic obstacles consideration       In  the  real  world,  dynamic  obstacles  can  only  be detected  when  robot  moves  approaching  that  obstacle using specific sensor. As such, the developed program can  only  detect dynamic obstacles when simulation of robot  movement  is  executed.  At  any  time  during simulation,  when  dynamic  obstacle  appears,  the  robot stops,  some  parameters  are  changed  and  the  second component of the program will be immediately executed to  find  new  path.  The  changed  parameters  include number of gene for each chromosome, initial coordinate based on the latest robot coordinate and new obstacles’ coordinate matrix. When the new path is acquired, the robot  will  move  again  until  another  dynamic  obstacle turns up. The process repeats until the robot reaches the final position.  5. Simulation result and comparison     Few  simulations  were  conducted  to  analyze  the effectiveness  of  GA  technique  as  the  path  planning algorithm. Both 4-way movement and 8-way movement approaches were simulated and presented. At the end, the result from both approaches is compared.  5.1. Simulation in environment with static obstacles   For  the  first  experiment, only static obstacles were added  into  simulated  environment.  The  initial coordinate is set to 0,0 meanwhile the final coordinate is randomly  set.  Here,  coordinate  15,9  is  selected  which means the final coordinate has distance 15 units to east and 9 units to south from the initial coordinate. Figure 4 shows the simulation results using 4-way movement and 8-way movement robots. The first simulation was done with  random  cluttered  obstacles  (figure  4a  and  4b). Then, the second simulation was executed with regional start Read gene[i] of a chromosome i=Add O with 1 gene[i] = gene[i+1] ? Add C with 1 i < total genes? Calculate cost function using equation 1 stop Set initial coordinate = 0,0 Update coordinate based on gene[i] value New coordinate = obstacle coordinates? i+Yes Yes No No No Yes obstacles (figure 4c and 4d) and the final simulation was performed with obstacles surrounds the final coordinate (figure 4e and 4f).    From figure 4, it has shown in general the capability of  GA  to  find  the  optimum  path  regardless  of  any formation  of  static  obstacles.  In  comparison,  8-way movement robot gives better path from 4-way movement robot with smaller changes of direction.   Simulation 1- Cluttered obstacles                4-way movement     8-way movement                   (a)                                           (b) Simulation 2 – Regional obstacles                4-way movement     8-way movement            (c)               (d) Simulation 3 – Obstacles surround final point                4-way movement     8-way movement           (e)               (f)   Figure 4. Simulation results in environment with static obstacles  5.2. Simulation in environment with static and dynamic obstacles     In order to investigate the efficiency of the whole program,  static  and  dynamic  obstacles  were  added  up into  the  environment.  Static  obstacles  were  defined randomly and the first component of the program was executed before simulating the robot movement. Later, during  the  simulation,  two  dynamic  obstacles  were added  in  two  different  periods  and  the  second component of the program as elaborated in section 4 is executed.  Figure  5  illustrates  the  simulation  results using 4-way movement and 8-way movement robots. In figure  5(a)  and  5(b),  the  original  paths  were  defined using  the  first  component.  When  a  dynamic  obstacle was added, the paths have changed as in figure 5(c) and 5(d)  accordingly.  Again,  when  the  second  dynamic obstacle  was  detected,  the  second  component  was executed to find the latest paths that should be taken by the robots (figure 5e and 5f). Based on these results, it has shown that the proposed algorithm was able to find the best path and avoid any obstacles exist on the path way.  Simulation 4 - Original path                4-way movement     8-way movement                   (a)                                           (b) Simulation 5 - Updated path (dynamic obstacle #1 detected)                 4-way movement     8-way movement                   (c)                                           (d) Simulation 6  - Updated path (dynamic obstacle #2 detected)                4-way movement     8-way movement                   (e)                                           (f)    Figure 5. Simulation results in environment with static and dynamic obstacles Static obstacle Final point Initial point Static obstacle  Dynamic obstacle Path 5.3 Analysis on algorithm efficiency level   The experiments in section 5.1 and 5.2 have shown the capability of the GA-based path planning algorithm to determine the optimum path for a robot to reach final position.  Another  experiment  was  also  conducted  to compare  the  path  produced  by the algorithm with the real optimum path identified manually. Figure 6 shows various paths comparison. According to figure 6(a), 6(b) and  6(f),  the  path  generated  by  the  GA-based  path planning algorithm is completely matched with the real optimum  path.  For  simulation  8  and 9 in figure 6(c), 6(d) and 6(e), the results from the algorithm are slightly different but nearly approaching the real optimum paths which can be considered as accepted variation. Table 1 indicates  the  differences  between  the  generated  paths from the algorithm and the real optimum paths.  Simulation 7 - Environment with no obstacle                 4-way movement     8-way movement                   (a)                                           (b)  Simulation 8 - Environment with cluttered obstacles                4-way movement     8-way movement                   (c)                                           (d)  Simulation 9 - Environment with regional obstacles                4-way movement     8-way movement                   (e)                                           (f)  Figure 6. Various paths comparison  Table 1. Differences between the generated paths from GA-based path planning algorithm and the real optimum paths  Simulation #  No. of changes of direction No. of unit distance taken to reach final point   Real  GA-based  Real  GA-based #7 (4-way) Figure. 6(a) 1  1  24  24 #7 (8-way) Figure. 6(b) 1  1  15  15 #8 (4-way) Figure 6(c) 4  6  24  24 #8 (8-way) Figure 6(d) 2  4  15  15 #9 (4-way) Figure 6(e) 1  3  24  24 #9 (8-way) Figure 6(f) 1  1  15  15    Another comparison that can be made is the results produced  by  4-way  movement  and  8-way  movement approaches.  From  Table  2,  it  can  be  seen  that  8-way movement  approach  generates  shorter  path  distance compared  to  4-way  movement  approach.  Number  of changes of direction is also smaller in 8-way movement approach  which  reduces time taken for robot to reach final location. Therefore, it can be concluded that 8-way movement  approach  is  better  than  4-way  movement approach.  6. Future work     To  further  improve  the  algorithm  proposed  in  this paper, some improvements are planned. The focus of the improvements  includes  the  study  to  develop  hybrid algorithm  of  GA  with  other  technique  and  also determine better approach to find appropriate number of genes in a chromosome so that level of efficiency can be further increased.    7. Conclusion     In conclusion, the path planning algorithm has been successfully developed using GA technique. It is proven based  on  the  discussed  result,  the  optimum  paths produced by the algorithm can move a robot from initial location to final location without collide with static or dynamic obstacles.        References  [1] Diosi A. et.al., "Interactive SLAM using Laser and Advanced  Sonar",  Proc.  International  Conference on Robotic and Automation, pp. 1103-1108, 2005. [2]Haupt  R.L  &  Haupt  S.E.,  Practical  Genetic Algorithms, John Wiley & Sons, 1998. [3] Masehian E. & Habibi G., “Robot Path Planning in 3D  Space  Using  Binary  Integer  Programming”, International  Journal  of  Mechanical  Systems Science  and  Engineering, Vol.1, No. 1, pp. 26-31, 2007.  [4] Negnevitsky M., Artificial Intelligence: A Guide to Intelligent Systems, Addision-Wesley, 2005. [5] Stentz A., “Optimal and Efficient Path Planning for Partially-Known  Environments”,  Proc.  International  Conference  on  Robotic  and Automation, pp. 3310-3317, 1994.        

Genetic Algorithm-based Robot Path Planning

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