Effective implementation of a mapping swarm of robots

Churavy, C.; Baker, Matt; Mehta, Saurabh; Pradhan, Ishu; Scheidegger, N.; Shanfelt, S.; Rarick, R.; Simon, Dan

doi:10.1109/mpot.2008.924855

Cited by 14 publications

(5 citation statements)

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“…Proximity sensors sense the object or surrounding material or other moving swarm robots without any physical contact, and calculate the precise distance of that object [8]. This crucial component not only avoids collision, but also prevents the physical damage to the swarm robots and maintains safe distance [2,3]. Depending on the type of technology used, proximity sensors are classified into different categories such as, inductive, capacitive, photoelectric, and ultrasonic proximity sensors.…”

Section: A Proximity Sensorsmentioning

confidence: 99%

UB robot swarm — Design, implementation, and power management

Patil

Abukhalil

Patel

et al. 2016

2016 12th IEEE International Conference on Control and Automation (ICCA)

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Abstract-In this paper we describe the hardware architecture of an inexpensive, heterogeneous robot swarm, designed and developed at the RISC lab, University of Bridgeport. Each swarm robot is equipped with sensors, actuators, control and communication units, power supply, and interconnection mechanism. This article also describes the essential features and design of a power distribution and management system for a dynamically reconfigurable system. It further presents the empirical results of the proposed power management system collected with the real robotic applications.

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Section: A Proximity Sensorsmentioning

confidence: 99%

UB robot swarm — Design, implementation, and power management

Patil

Abukhalil

Patel

et al. 2016

2016 12th IEEE International Conference on Control and Automation (ICCA)

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“…Our robots were originally assembled in 2007 [22], but we have adapted them to our work [4]. Our robots are designed to be flexible.…”

Section: Hardwarementioning

confidence: 99%

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

Thomas

Lozovyy

Simon

2011

Lecture Notes in Computer Science

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Abstract. Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.

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“…Autonomous mobile robots are supplied using batteries mounted on their platform in order to provide power to the onboard sensors, microcontrollers, servos, and peripheral modules. Batteries have a specific charge rate and hence, the operational time of the robots in the swarm is restricted [4]; therefore, a successful utilization of power resources is vital for managing the remaining energy in the robot's group [5]. e total power consumed by each individual robot can be calculated by multiplying the current consumed by each components by their working time [6].…”

Section: Introductionmentioning

confidence: 99%

Power Optimization in Mobile Robots Using a Real-Time Heuristic

Abukhalil

AlMahafzah

Alksasbeh

et al. 2020

Journal of Robotics

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Mobile robots typically run using finite energy resources, supplied by finite batteries. The limitation of energy resources requires human intervention for recharging the batteries. To reduce human intervention, this work focuses on coordinating power in a group of robots. A power optimization subroutine provides some sense of distribution of power by the control unit (CU). A variety of on-board sensors, actuators, and communication modules are controlled by a heuristic-based controller class allowing such components to conserve the current taken from the attached power source. Using the proposed approach, autonomous robots will be aware of their power system, especially regarding battery life. The new approach takes advantage of a heuristic function which uses evaluation values calculated at different times for the different robots. An experimental setup is applied on the team of robots. The optimization module is evaluated on each robot. The results show that the team of mobile robots consumes less energy and more efficient power regulation during their duties. Finally, the application of the proposed optimization technique in a distributed manner achieves good power saving figures when performing the particular task.

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Effective implementation of a mapping swarm of robots

Cited by 14 publications

References 0 publications

UB robot swarm — Design, implementation, and power management

UB robot swarm — Design, implementation, and power management

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

Power Optimization in Mobile Robots Using a Real-Time Heuristic

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