Path Planning of Scorbot-ER 4U Robot

 

Prof. P.K. Sharma¹, Mr. Ashish Paul², Mr. Pradeep Dwivedi ³

NRI Institute of Science and Technology, Bhopal, M.P

*Corresponding Author E-mail: erashu_paul2004@yahoo.co.in

 

 

ABSTRACT:

In the past mobile robot research was often focused to various kinds of point-to-point tasks. An original path tracking controller for a vision-based automated guided vehicle. The SCARA word stands for Selective Compliant Assembly Robot Arm .Reducing the positioning errors of a SCARA manipulator during the execution of a motion along a given trajectory the controller is used. Service tasks, such as floor cleaning, require specific approaches for path planning and vehicle guidance in real inside areas like homes, offices and industries. The paper will discusses automatic planning of a feasible cleaning path considering a 2D-MAP as well as kinematic and geometric robot model. Path construction makes use of two typical motion patterns. Each pattern is defined by a sequence of sub goals indicating robot position and orientation. If obstacles permanently block the preplanned path, an automatic map update and path replanning is performed.  

 

 

1. INTRODUCTION:

The reduction of the positioning errors of a manipulator during the execution of a motion along a predefined trajectory is a very important research topic in Robotics; indeed many industrial applications require that the robot gripper follows an assigned trajectory with high precision. Sometimes it is also necessary to execute the motion in a prefixed time, usually imposed by the productivity requirements of the plants where the machine is installed. In practice, nevertheless, there are many factors that prevent the robot from following the trajectory with the desired level of precision: among them, the vibrations due to the elasticity of the mechanical members are undoubtedly one of the main causes of inaccuracy. Older cleaning robots usually featured simple behaviours   made by a random path scheduling algorithm, which is sometimes combined with programmed trajectory patterns such as spirals and serpentines and a wall following mechanism. Those that had a better algorithm for producing their path were a lot more expensive than their randomly moving competitors [2]. In [1] the coverage of some simple commercially available robots was measured.

 

The maximum area covered turned out to be dependent on the robots shape: "The maximum area covered is highly correlated with how close the robot can go to the walls and the radial distance from the cleaning device to the external structure of the mobile robot" path planning approach that determines a path that includes every reachable point in the target environment [3]. To make an efficient path through its environment, a cleaning robot therefore needs to use a good coverage planning algorithm.

 

2. TRADITIONAL ROBOT CONTROL TECHNIQUES:

Perhaps the obvious process for programming robot controllers would be to build a model of the system and its environment, and then use appropriate planning techniques to design a program for the controller which perfectly carries out the desired tasks in the fixed environment. Typically this would involve controlling the position of, and forces exerted by, a robot manipulator, with constraints on the paths travelled and smoothness of movements. Clearly, such model-based methods will not be well suited to autonomous robots that work in dynamical environments with unknown details, and   have to suited to autonomous robots that work in dynamical environments with unknown details, and have to cope with factors such as unpredictable payload variations, plant degradation, and so on. To overcome this limitation, a sensor-based approach is a natural alternative.

 

3. PATH PLANNING:

A simple way of covering the entire terrain is to make the robot move in an arbitrary direction until the current path is obstructed. Once the robot cannot continue in its current direction this procedure is repeated. This is a random path planning algorithm. It is sometimes combined with programmed trajectory patterns such as spirals and serpentines and/or a wall following mechanism to increase its efficiency. It has been proven that as the cleaning time goes to infinity a random path algorithm will eventually reach the maximum coverage [1]. Path planning has its advantages: It does necessitate a lot of computations to calculate a path, and few sensors are needed. Additionally the robot does not need to use a map of its environment. Another large advantage of random path planning is that the algorithm can reach maximal coverage in any environment, regardless of the shape, even if the environment is dynamically changing and unknown. It is however inefficient as the path can have considerable overlap with itself [4]

 

4. ANALYSIS:

The robot ability to learn about its environment was added to a stock robot. Through the use of data gathered from unmodified trial runs using the preprogrammed subsumption architecture the robot was able to build a map of its environment without any additional sensors. Use of the data provided by the robot during a cleaning cycle for map generation was proven to be reasonably effective. The lack of small changes in trajectory limited the effects of truncation error. This error was further mitigated by the addition of half a degree added to each reported measurement (which assumed a symmetric distribution of fractional degrees prior to truncation).The robot was able to determine which parts of free space.

 

5. SOLUTIONS:

The simplest case of coverage planning is covering an empty room. Two standard ways of covering an empty room are using a spiral pattern and using back and forth-motions. Both of these patterns can be a basis for an coverage algorithm that works in general environments. These basic patterns are shown in figure -a. In these figures the path made is indicated in black lines. The robot is drawn at its final position to indicate its size in comparison with the environment. The covered area is indicated in grey. Area left uncovered is coloured white. A third common way of covering a simple room is by making use a random path. Its main advantage is that it works on any room regardless of its shape. It is also the simplest algorithm, which is why we discuss it first.

Fig (a)

 

6. CONCLUSION:

In this paper a small overview of some path planning algorithms was given along with their strengths and Weaknesses Most coverage algorithms use a cellular decomposition to ensure complete coverage. In this work the Boustrophedon Cellular Decomposition was expanded to handle dynamic, unknown environments to create a coverage path planning algorithm suitable for cleaning robots. We first showed how for a static known room an optimal sequence in which to cover every cell of an environment can be computed, by formulating the corresponding optimalization problem as a travelling salesman problem.

 

7. REFERENCES:

1.       Palleja T., Tresanchez M., Teixido M., Palacin J. "Modeling floor-cleaning coverage performances of    some domestic mobile robots in a reduced scenario", Robotics andAutonomous Systems, Volume 58 Issue 1, January, 2010 ,pp.37-45

2.       Prassler E., Ritter A., Schaffer C., Fiorini P.."A Short History of Cleaning Robots", Autonomous Robots, Volume 9

3.       Choset H. "Coverage for robotics – A survey of recent results", Annals of Mathematics and Artificial Intelligence 31: 113–126, 2001.

4.       Acar E. U., Choset H., Zhang Y., Schervish M.   “Path Planning for Robotic Demining: Robust Sensor-Based Coverage of Unstructured Environments and Probabilistic Methods” The  International Journal of Robotics Research July.

5.       WIKIPEDIA. Roomba — wikipedia, the free encyclopedia,2008. [Online; accessed 11-December-2008]..

6.       Choset H. "Coverage for robotics – A survey of recent results", Annals of Mathematics and Artificial Intelligence 31:113–126, 2001.

7.       Choset H., Pignon P. "Coverage Path Planning: The Boustrophedon Cellular Decomposition", International Conference on Field and Service Robotics 1997

8.       Friggstad Z., Salavatipour M.R., Svitkina Z., "Asymmetric traveling salesman path and directed latency problems", Proceeding SODA '10 Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms

9.       Gonzalez, E. Alarcon, M. Aristizabal, P. Parra, C. “BSA: a coverage algorithm“, Intelligent Robots and Systems, 2003. IEEE/RSJ International Conference, 27-31 Oct. 2003, page(s): 1679 - 1684 vol.2

 

 

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