Minimal Energy Path Planning for Wireless Robots

Ooi, Chia Ching; Schindelhauer, Christian

doi:10.1007/s11036-008-0150-5

Cited by 60 publications

(58 citation statements)

References 17 publications

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“…Thus, a robot needs to efficiently plan the usage of its limited energy for its motion, communication, sensing and computation during the operation. Among these, it is reported that motion and communication are two major consumers [11]. This is the main motivation of the work proposed here.…”

Section: Introductionmentioning

confidence: 91%

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Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Yuan

Mostofi

2013

IEEE Trans. Wireless Commun.

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Abstract-We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a pre-defined trajectory, and while minimizing its motion and communication energy costs. We propose a co-optimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavy-task load and the light-task load cases. We also propose an additional stop-time online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement.

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Section: Introductionmentioning

confidence: 91%

“…In [11], the authors propose an efficient algorithm to find the path that minimizes the motion and communication energy costs. However, simplified path loss models are utilized to model the communication channels.…”

Section: Introductionmentioning

confidence: 99%

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Yuan

Mostofi

2013

IEEE Trans. Wireless Commun.

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“…Several studies [11], [12], [17] propose using mobile sensors as data mules that pick up data from the sensors and transport it to the BS. However, similar to the case of mobile BSs, the delay of data delivery is often large due to the intermittent contacts between data mules and other nodes.…”

Section: Related Workmentioning

confidence: 99%

Maximizing data gathering capacity of wireless sensor networks using mobile relays

El-Moukaddem

Torng

Xing

2010

The 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE MASS 2010)

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Abstract-Recently, the availability of numerous low-cost robotic units (e.g., Packbot, Robomote, and Khepera) has made it possible to massively deploy mobile sensors in a network and use them in a disposable manner. It has been shown that the controlled mobility offered by sensors can be exploited to improve the energy efficiency of a network. In this paper, we study a new problem called max-data mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. Different from previous controlled mobility approaches, we account for several characteristics of existing practical mobile sensing platforms including limited mobility and the high energy consumption of locomotion. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies. Our extensive simulations based on realistic energy models of existing mobile sensing platforms show that our approach can increase the data gathering capacity by a factor of at least 2 in most scenarios. Moreover, our distributed algorithms converge quickly and incur low messaging overhead.

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“…Recently, the optimization problem of motion and communication cost was studied in [15]. Taking a single-robot single-base station scenario, they solved the problem by using a proposed approximation algorithm based on Dijkstra's algorithm to compute the minimal energy path, and show that up to nearly 50% of total energy can be saved.…”

Section: Related Workmentioning

confidence: 99%

“…For robot motion, we adopt the energy model E m = m d m , where d m is the distance traversed by the robot in meter and the movement parameter, m, in Joule/meter, is a constant value determined by the mass of the robot, the friction to the surface, gravity and acceleration. This model is suitable for wheeled robot [12], and is adopted by [13,15,20].…”

Section: Energy Models For Robotmentioning

confidence: 99%

Detours Save Energy in Mobile Wireless Networks

Ooi¹,

Schindelhauer²

Wireless and Mobile Networking

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Autonomous robotic systems have been gaining the attention of research community in mobile ad hoc network since the past few years. While motion cost and communications cost constitute the primary energy consumers, each of them is investigated independently. By taking into account the power consumption of both entities, the overall energy efficiency of a system can be further improved. In this paper, the energy optimization problem of radio communication and motion is examined. We consider a hybrid wireless network that consists of a single autonomous mobile node and multiple relay nodes. The mobile node interacts with the relays within its vicinity by continuously communicating high-bandwidth data, e.g. triggered by a multimedia application like video surveillance. The goal is to find the best path such that the energy consumption for both mobility and communications is minimized. We introduce the Radio-Energy-Aware (REA) path computation strategy by utilizing node mobility. Given the starting point, the target point and the position of the relays, our simulation results show that the proposed strategy improves the energy efficiency of mobile node compared to the Motion-Energy-Aware (MEA) path constructed based only on the mobility cost.

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Minimal Energy Path Planning for Wireless Robots

Cited by 60 publications

References 17 publications

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Maximizing data gathering capacity of wireless sensor networks using mobile relays

Detours Save Energy in Mobile Wireless Networks

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