Autonomous Robots Which Work Computer Science

Essay add: 30-03-2016, 15:27   /   Views: 8

Autonomous robots which work without human operators are required in robotic fields. In order to achieve tasks, autonomous robots have to be intelligent and should decide their own action. When the autonomous robot decides its action, it is necessary to plan optimally depending on their tasks. The problem of controlling a mobile robot and planning its trajectory is dominated by the fact that the robot's knowledge of the world is constantly in flux. This is caused by the variation of the environment itself, by error in the robot's reckoning of its own position over time, and by its poor ability to make accurate and precise measurements of the environment.

As the field of robotics has developed, increasingly more of these challenges have been addressed at a time. In the 1950s, the earliest robots were able only to achieve basic locomotion. In the 1970s and 80s, the optimal completion of global objectives dominated the active research. Later, real-time constraints gained attention at the cost of optimal planning. Field robotics has moved in

recent years out of controlled laboratory and office environments into outdoor environments where less and less a priori knowledge of the structure of the world can be assumed. Sensing has shifted from SONAR and IR, to accurate

and model able LIDAR scanning, to color vision, both within and beyond stereo range. Moreover, robots have accelerated drastically, from 1969 Shakey's 2 meters/hour to 2005 Stanley's 70 miles/hour.

The robotics community continues to be challenged by several issues:

unknown unstructured environments,

sensor errors,

control constraints,

real-time guarantees,

global objectives,

dynamic obstacles

Mobile robots are becoming increasingly common in today's society. Their applications are as diverse as the scientifically inspired NASA Mars Rovers to domesticated robot vacuum cleaners. Regardless of the application, path planning is an integral part of the design of mobile robots. The objective of path planning is to move the mobile robot from a known starting position to a desired end position while avoiding obstacles, such as humans, barricades or other mobile robots.

1.2.Overview of Chapters

Chapter 1 deals with the brief introduction about the research field

Chapter 2 deals with the literature survey. This chapter tells what work has been

previously done in the field of path planning of robot.

Chapter 3 gives the purpose of the proposed research work and the tasks to be done in the research.

Chapter 4 discusses the representation and the approach used by us in the problem

Chapter 5 defines the method followed to solve the problem using Genetic Algorithm.

Chapter 6 deals with the division of the problem into two sub problems and finding the solution

of each sub problem moving to the complete solution of the main problem


The path planning problem is typically approached using a method of one of three categories:


sampling-based, or


By far, search-based methods currently dominate path planning in field robotics. The popularity of this method can be attributed first to the relative ease of its implementation and second to the early establishment of dynamic search-based algorithms. The sampling- based methods are relatively new to robotics and are the subject of a rush of recent research. These methods are particularly useful for planning of serial manipulators and in other situations where higher-dimensional planning is required. The combinatorial methods are the oldest and perhaps most studied branch of planning, having applicability in many areas, ranging from computer graphics to VLSI design.

2.1.1.Search-based Planning

A grid of regularly sized grid cells is used to represent the configuration space. The goal and robot locations are known within the grid and a search is run on the grid to solve the point-to-point find-path problem.

Figure 2.1. Search Based Planning

2.1.2 Sampling-based Planning

The sampling-based methods follow the general form.:(1) a new vertex v is generated. (2) A check is performed to determine if v is in the interior of an obstacle. If it is, either the cycle restarts or the vertex is somehow projected to the boundary of the obstacle. (3) When a good vertex is known, edges are added between it and its mutually visible immediate neighbors (usually its k-nearest neighbors or those neighbors in some neighborhood of v). Eventually, the graph contains a path between the start and goal vertices.

Figure 2.2. Sampling Based Planning

2.1.3. Combinatorial Planning

Combinatorial path planning essentially takes as input a polyhedral

representation of the environment and augments it with additional edges or

vertices. The result is a graph known as a roadmap. Whereas the sampling-based method populated free space with new vertices, combinatorial methods use the boundary of the polygonal obstacles directly.

Figure 2.3. Combinatorial Based Planning

Much of the prior work in robot path planning done for stationary obstacles

makes use of methods to generate an optimal path such: dynamic

Article name: Autonomous Robots Which Work Computer Science essay, research paper, dissertation