NDLI: A multi-vehicle testbed for multi-modal, decentralized sensing of the environment

Content Provider	IEEE Xplore Digital Library
Author	Cortez, R.A. Luna, J.-M. Fierro, R. Wood, J.
Copyright Year	2010
Description	Author affiliation: Department of Mechanical Engineering, University of New Mexico, Albuquerque, 87131-0001, USA (Cortez, R.A.; Wood, J.) \|\| MARHES Lab, Electrical & Computer Engineering Department, University of New Mexico, Albuquerque, 87131-0001, USA (Luna, J.-M.; Fierro, R.)
Abstract	This video presents experiments conducted on our multi-vehicle environmental testbed. The first experiment is the prioritized multi-sensing behavior, which uses a magnetic and light source for the robots to take sensor measurements. During the experiment robots create a Voronoi partitioning based on their current positions, then optimize the probability of detection map (POD) to determine a point in their own Voronoi partition that has the highest probability of obtaining good sensor readings from a magnetic or light source. When a point by each robot is identified, the robots navigate to that goal point while avoiding collisions with obstacles in the environment as well as with other robots. As the robots navigate to the goal point sensor measurements are taken. When each robot has reached its destination, sensor measurement information as well as position information is exchanged. This is then repeated for 30 iterations and the resulting magnetic and light intensity maps are shown. The second experiment conducted is the adaptive decentralized nonholonomic sensor network algorithm. Initially the robot at the right corner is exposed to the greatest concentration of light. Light measurements are taken using four precision light sensors mounted on the aluminum plate. Every robot is moving to their estimated center of mass of its respective Voronoi cell. As they are using a difference equation approximation of the adaptive law we notice back and forth motion. This is due to the centers of mass variation at every calculation time. In another top view, we see the actual distribution of the robots and their Voronoi partitions related to the light concentration. This is approximately a centroidal Voronoi tesselation based on the estimate distribution function which is illustrated as well. The light distribution is changed by switching the light source concentration. At this point the robots change their estimate parameter vectors in order to excite the adaptation law by increasing the difference between what they are measuring and what they are estimating. The robots then start moving towards the new light concentration. Finally, a top view of the robots with respect to the light distribution and the estimated density function are shown. This experiment verifies the behavior of the adaptive decentralized nonholonomic sensor network controller.
Starting Page	1088
Ending Page	1089
File Size	779639
Page Count	2
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509315
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic sensors Robot sensing systems Navigation Robot kinematics USA Councils Vehicles Automatic testing Global Positioning System Grippers Mechanical sensors
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-vehicle testbed for decentralized environmental sensing

A deliberative architecture for AUV control

Collective Sensing with Self-Organizing Robots

Ultrasonic relative positioning for multi-robot systems

Reinforcement learning with function approximation for cooperative navigation tasks

Multi-objective optimization of sensor quality with efficient marine vehicle task execution

Decentralized feedback controllers for multi-agent teams in environments with obstacles

Automatic formation deployment of decentralized heterogeneous multi-robot networks with limited sensing capabilities

Lane level positioning using particle filtering

A multi-vehicle testbed for multi-modal, decentralized sensing of the environment

Similar Documents

Multi-vehicle testbed for decentralized environmental sensing

A deliberative architecture for AUV control

Collective Sensing with Self-Organizing Robots

Ultrasonic relative positioning for multi-robot systems

Reinforcement learning with function approximation for cooperative navigation tasks

Multi-objective optimization of sensor quality with efficient marine vehicle task execution

Decentralized feedback controllers for multi-agent teams in environments with obstacles

Automatic formation deployment of decentralized heterogeneous multi-robot networks with limited sensing capabilities

Lane level positioning using particle filtering

A multi-vehicle testbed for multi-modal, decentralized sensing of the environment