Deployment Strategy for Mobile Robots with Energy and Timing Constraints
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Transcript of Deployment Strategy for Mobile Robots with Energy and Timing Constraints
Deployment Strategy for Mobile Robots with Energy and Timing Constraints
Yongguo Mei, Yung-Hsiang Lu, Y. Charlie Hu, and C.S. George Lee
School of Electrical and Computer Engineering, Purdue University
ICRA 2005 (IEEE International Conference On Robotics And Automation)
Outline
• Introduction• Deployment strategy :
• Overhead deployment• Area covered by one group• Number of groups and group size• Deployment algorithm
• Experimental Evaluation• Conclusions
Introduction
• Mobile robots carry limited energy sources• A lot of tasks have timing constraints
• Search survivors and rescue• Landmine detection
Motivation
• Few studies have been conducted for deploying mobile robots• The number of robots needed• Initial locations of these robots
Are affected by each robot’s energy capacity, the deadline, and the moving speed
Goals
• The desirable deployment strategy• Uses the minimum number of robots to
cover a given area• cover the area within the energy and the
timing constraints
Explains the rules to find better deployment strategies for reducing the number of robots in each group and the number of groups
Assumption
• All robots are the same• Initial energy , each robot’s power consumption is af
fected only by its speed• Sensing range is d , sensing region is 2d * 2d = 4d2
• The robots travel along scanline to cover the area• The area to be covered is a two-dimensional region
without obstacles
2d
2d
Sensing
Area
Scan-Line
2d
Overhead deployment
• Unloading time : a robot that is unloaded later has shorter time before the deadline
• Dispersing overhead : the time and the energy spent by each robot to reach its starting location after being unloaded
• Fragmentation overhead : when a robot can’t finish a scanline due to energy or timing constraints or both
Overhead deployment
• unloading time: longer time to unload m
ore robots
• dispersing overhead:
AB and AC
• fragmentation overhead: from D to E
The fragmentation overhead in terms of area is at most 2dh
Area Coverage by One Group
An area covered by a group of 12 robots
• Point A is the unloading location of the whole group
• The 5th robot spends time traveling across AB, its covered area can’t be larger than a1
Area Coverage by One Group
• Average dispersing overhead: w/2• Average fragmentation overhead: h/2• h = w minimize the total overhead
• The total dispersing distance
0 + AB + AD ≈ 0 + w/3 + 2w/3
= w
• w /ψ+ 2w /ψ +…+ (ψ -1)w/ ψ
= (ψ -1)w/ 2
Deployment Strategy
• make the minimum areas of all groups close• unload fewer robots for later groups• minimize the size of the first group
Deployment AlgorithmEach group’s size depends on only the sizes of
the previous groups
The size for the first group
The sizes of the other groups
The size of the latest assigned group
Deployment Algorithm
Because the determination of the last groupsize depends only on the comparison of minimum areas,not the area left before unloading the last group
Simulation
• A commercial robot called PPPK is used • Omni-directional wheels dr
iven by three MS492MH DC servo motors
• Energy capacity:20736J (4 AA batteries)
• P(v) = 48.31v2 – 3.37v +0.69• Optimal speed: 0.12m/s with
power consumption 0.98W
Simulation
Area covered by different number of robots with differentratios of height and width
6 hours before deadline
Simulation
• Comparing with two other solutions• Equal-number deployment by unloading
the same number of robots each time• Unloads all robots at one location
• Sensing distance used is 0.8m
Deployment for covering 6.8 * 105 m2 within four hours
Simulation
With the same conditions, the one-unloading method has to use 416 robots, and the average area per robot is 1634m2.
Conclusions
• This paper presents a method to deploy mobile robots for covering an area with energy and timing constraints.
• Our approach determines the number of robots in each group and the number of groups.