22-08-2012, 12:46 PM
REPORT ON SNACK ROBOT
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INTRODUCTION
In the past two decades it is estimated that disasters are responsible for about 3 million deaths worldwide, 800million people adversely affected, and property damage exceeding US$50 billion. The recent earthquake in Turkey in November of 1999 left 700 dead and 5000 injured. Many of these deaths were from structural collapse as buildings fell down onto people. Urban Search and Rescue involves the location, rescue (extrication), and initial medical stabilization of victims trapped in confined spaces. Voids formed when a buildings collapse is one instance of a confined space. Urban Search and Rescue may be needed for a variety of situations, including earthquakes, hurricanes, tornadoes floods, fires, terrorist activities, and hazardous materials (hazmat) accidents. Currently, a typical search and rescue team is composed of about ten people, including canine handlers and dogs, a paramedic, a structural engineer, and various specialists in handling special equipment to find and extract a victim. Current state of the art search equipment includes search cameras and listening devices. Search cameras are usually video cameras mounted on some device like a pole that can be inserted into gaps and holes to look for signs of people. Often a hole is bored into the obstructing walls if a void is suspected to exist on the other side. Thermal imaging is also used. This is especially useful in finding warm bodies that have been coated with dust and debris effectively camouflaging the victim. The listening devices are highly sensitive microphones that can listen for a person who may be moving or attempting to respond to rescuers calls. This hole process can take many hours to search one building. If a person is found extrication can take even longer. This paper presents the developments of a modular robot system towards USAR applications as well as the issues that would need to be addressed in order to make such a system practical.
HISTORY
The idea of automata originates in the mythologies of many cultures around the world. Engineers and inventors from ancient civilizations, including Ancient China, Ancient Greece, and Ptolemaic Egypt, attempted to build self-operating machines, some resembling animals and humans. Early descriptions of automata include the artificial doves of Archytas, the artificial birds of Mozi and Lu Ban, a "speaking" automaton by Hero of Alexandria, a washstand automaton by Philo of Byzantium, and a human automaton described in the Lie Zi.
The word robot was introduced to the public by the Czech interwar writer KarelČapek in his play R.U.R. (Rossum's Universal Robots), published in 1920. The play begins in a factory that makes artificial people called robots, though they are closer to the modern ideas of androids, creatures who can be mistaken for humans. They can plainly think for themselves, though they seem happy to serve. At issue is whether the robots are being exploited and the consequences of their treatment.
KarelČapek himself did not coin the word. He wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he named his brother, the painter and writer Josef Čapek, as its actual originator.
SENSOR-BASED ONLINE PATH PLANNING
This section presents multisensor-based online path planning of a serpentine robot in the unstructured, changing environment of earthquake rubble during the search of living bodies. The robot presented in this section is composed of six identical segments joined together through a two-way, two degrees-of- freedom (DOF) joint enabling yaw and pitch rotation (Fig.), while our prototype mechanism (to be discussed later in this article) is made of ten joints with 1 DOF each.
MDT- BASED EXPLORATORY PATH PLANNING METHODOLOGY
The major aim of the serpentine search robot is to find and identify living beings under rubble and lock onto their signals until they are reached. Therefore, local map building is an essential component of our path planning approach. Since the objects in the rubble environment are expected to change position and orientation, the local map is used to find the next desired position of the robot on its way to a goal, the living being, placed in an initially unknown but detected location.[1]
The ultrasound sensor scans to determine obstacles and free space and develops a local map. Thus, sensory data constructs a local map within this sensor range. After the local map is obtained, the next possible intermediate goals are found by considering points that are at the middle of the arcs representing free space. The intermediate goal is selected from the candidate next states by considering the directions of the candidate states relative to the robot’s head. In real applications, the direction that gives the highest signal energy (thermal, sound) received from the goal (living being) is selected as an intermediate goal. The intermittent function of the camera is also used for choosing the most appropriate intermediate goal. However, in the simulation here, we represent, for illustrative purposes, the magnitude of the signals coming from the main goal as inversely proportional to the distance between sensor and goal. Thus, this distance becomes minimum when the robot sensor faces the goal that is an emulation of the maximum signal energy coming from the goal.
SIMULATION RESULTS
A sample locomotion sequence is shown in Fig. Since the robot starts its next gait with initial line up (reset) and ends the gait with a final line up, the robot is shown as a line in these local maps. In the last version of our method, this resetting is optional and can be omitted, allowing the snake-like robot to proceed into a new gait from the body shape acquired from the last accomplished gait. After the local map is built, the robot decides the next gait using MDT, then lines up if resetting is possible, simplifying computational load. If this is not possible, the snake robot directly implements the selected gait from its previously acquired body shape. Intermediate goals are used to proceed towards the main goal. Sample simulation results are shown in Fig.5, which shows the displacement of the snake-robot among obstacles. This sample shows the path followed by the robot as composed of lines and arcs that are the result of the serpentine gaits used by the snake like robot. Straight lines in the direction of the robot are formed by rectilinear motion. Short lines deviating from the main path are formed by flapping motions. The arcs on the path are formed by rotational motion.[1]