DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM

DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM FOR WHEELED
MOBILE ROBOT IN A KNOWN DYNAMIC ENVIRONMENT
BY
NITISH KOYYALAMUDI
K-ID: K00346319
INSTRUCTOR
DR. LIFFORD MCLAUCHLAN
DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

ABSTRACT
Must ensure an optimal path for mobile robot path planning. Best possible paths, time, energy
consumption, etc. Path planning of robots also depends on static or dynamic operating environment
such as a known or unknown. This article uses A * algorithm and genetic algorithm research on global
path planning. Known as a dynamic environment, and communicate the control station will calculate
the shortest path to mobile robots and a robot will traverse the path to achieving the goal.
Trace the path traversed by the robot control station. The shortest path for mobile robot navigation, if
robots detect doomed any obstacle in your path, mobile robots will update the information relating to
the environment, and place this information will be sent to the control station. Then the control station,
with updated maps and new starting position and recalculate the new shortest path of destination, if
any, and move to the robot, it can reach its destination. This technology has been implemented and in
the real world of experiment and simulation of a wide range of test run. Results show that technology
effectively calculates the known the shortest path in your environment in dynamic and allows robots to
complete tasks quickly.
Key Words: A* algorithm, path planning, mobile robot

1. INTRODUCTION
Path Planning of Mobile robot is usually stated as given the description of mobile robots and
environments, planned two specified locations, between start and end points of the path. Paths should
be free of bumps and some optimization condition is met (that is, minimum cost paths). According to
this definition, the path planning is classified as a problem of optimization. Researchers defined
different techniques as the solution for the path planning based on various factors like:
1. Environment type (i.e., static or dynamic),
2. Path Planning Algorithms (i.e., global or local).
The environment doesn’t contain any moving object is known as the static environment, in addition to
the navigation of robot moving dynamic environment with dynamic objects (that is, human beings,
mobile machines, and other mobile robots). Path Planning of the global algorithm requires all
environment is fully aware of the static environment. While, path planning of local refers to
implementing the path planning while the robot is on the move; other words, A* algorithm is having an
ability of new path-finding accordingly to the changes in the environment.

1. SYSTEM ARCHITECTURE
Wheeled mobile robot composed of an infrared range finder, position encoder, and communication
module soon. 3 Infrared range finder detects obstacles were placed on the RIGHT side, FRONT side
and LEFT side of the robot, as in Figure.1. Position encoders allow a robot to measures itself from the
location how it is passed from one location. With Communication unit, (Xbee) allows robots to give
and take information.
Figure1: Wheeled Mobile Robot
Grand Central Terminal by the PC desktop and the (Xbee) wireless unit. Path planning algorithm for
central train station will be running will be wirelessly transmitted to a robot. The environment is
assumed to be known as grid environment, here the robot's position expressed in a Cartesian coordinate
system of two-dimensional. The motion of Robot is assumed to be in horizontal and vertical direction
isn't considered to be the movement of diagonal.
The site and assumptions are given below:
 Mobile robot is assuming as a point in time and size occupies only one cell
 A series of sensors are equipped, position encoders and communications set
 4- directions means NORTH, EAST, SOUTH, WEST are movable

1. DESIGN AND IMPLEMENTATION
3.1 Central Station
Desktop as a central control station, A* algorithm for the calculation of the shortest path. The algorithm
output is moving (forward, backward, left, right, etc) sequences were communicated through wireless
communication (Xbee) mobile robot. RS-232 serial port is applied where the pins will Transmit (Tx)
and Receive (Rx). Both Coded algorithm and serial port are accessed with “C++”. Serial port is joined
by a serial cable to the (Xbee) communication unit.
3.2 Mobile Robot
Embedded in “C” programming for mobile robots, navigation using the position encoder the desired
path. Distance or angle of rotation can accurately control by the position encoder. Commands that can
be received wirelessly with the Xbee from the central train station. Once get on to the target, mobile
robots will give to the Central unit.
3.2.1 Localization of Robot
Initial direction is EAST assumes for that robot. Mobile robot and then calculate its position using an
algorithm in the grid. I and j represent the x and y axis movement respectively. Figure.2 shows the
position values for j and i if it has changed, and to move in various directions.
Robots initial direction is assumed as EAST. The mobile robot will then calculate its position in the
grid using an algorithm. Let i and j represent the movement in x and y axis’s respectively. Figure.2
shows the change in location values i and j, while moving in different directions.
Figure.2: Robots Orientation

ORTN values, N, S, E and W said the direction of the robot in the North, South, East and West,
respectively. ROTN on behalf of the robot spin around and F, R, L, the robot movement. Assuming the
initial orientation of the robot to the East is the ORTN = E. Robot localization pseudo code is as below
1) If ROTN=F and ORTN=E, then ORTN=E and i++;
2) If ROTN=R and ORTN=E, then ORTN=S and j++;
3) If ROTN=L and ORTN=E, then ORTN=N and j-- ;
Robot motion every time it updates its value orientation and position values in the x and y axis. Robots
continue to look for obstacles in the path. Figure.4 shows Setup flow chart.
Figure.3: Flow chart

1. EXPERIMENTAL SETUP
Size of the mesh (20x20) cm arrangements, which obstacles are saved and fed to the central station.
Central station will calculate the shortest route, which would communicate wirelessly to the robot and
begin by specifying the path for mobile robots. If the robot detects obstacles in the specified path, the
robot stops moving; their location and obstacle location. Such information and directions will be sent to
the central unit. Update of table's central railway station, assigns the current robot position as a starting
point. Central station rerun algorithms and smallest path if one robot will be notified. The 5×3 grid is
applied. Obstacles matrix provides information of current obstacles in a known environment. Nodes of
start and destination nodes are given.
Central station inputs contain:
1. Source location
2. Destination location
3. Giving the obstacle location in the Obstacle table
Figure.5: Output window at central station
The output central station has:
1. Time to Computation
2. Routing movement and
3. Commands

Figure.6: Shortest path planning scenario in static environment
Path planning algorithm in Figure.6 a dynamic environment with known experimental devices. Setting
the start position is supply as a source to a central unit. Hurdles table, namely the size 5 x 3 are
initialized to 0 or 1 according to the obstacles. In addition, to initializing destination point.
Figure.7: Shortest path planning scenario in dynamic environment

Experimental setup in Figure.7 of path planning algorithm in dynamic environments, also in the third
obstacle placed on a grid. To the target process, the robot detected the third obstacle, then it will be
localized, and unobstructed location. This communication is transmitted to the central unit, runs
programming algorithm of the shortest path using the updated information, which will enable the
destination smallest path traversal for mobile robots.

1. CONCLUSIONS
Path planning in a familiar environment is constructed and executed. The robot can travel to the target
by the smallest path. It was found to have deviated from the actual and desired paths. This is because of
the wheel slip position measuring system fault, battery charge fluctuation differences and friction in
between the wheel and the path, use of position while avoiding and mapping technology as the
extended Kalman filter, from the path of deviation, particulate filters, can predict the probability and
statistics methods, it can be amended accordingly.

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