Energy-time-efficient adaptive dispatching algorithms for ant-like robot systems

Chang, Hao; Lee, C.S. George; Lu, Yung-Hsiang; Hu, Y. Charlie

doi:10.1109/robot.2004.1308762

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…Subsequently, a multi-objective function is used that linearly combines energy and time into a simple single parameter-free metric, the Energy-Time-Product (ETP), measured in Joule-Seconds (Js) [2]. The ETP is inspired by the Power-Delay Product frequently used in electronics engineering.…”

Section: Resultsmentioning

confidence: 99%

“…For simple robots with decentralised control, Chang et al [2] deployed robots based on perceived local environment size, reducing unnecessary locomotion. When larger areas are discovered, additional robots are requested to aid exploration, reducing search time.…”

Section: Related Workmentioning

confidence: 99%

“…Three strategies are compared that are scalable, decentralised, and require low computational and communication resources. The strategies exploit environment information as it is acquired by the robots to reduce unnecessary locomotion [7,2], and reduce diminishing returns and interference between robots [20,9]. A video showing the three deployment strategies is available online 3 .…”

Section: Deployment Strategiesmentioning

confidence: 99%

“…This was used in our prior work [22] and is similar to the linear dispatching presented by Chang et al [2]. Longer inter-launch intervals (λ ) slow deployment, but decrease the number of concurrent flying robots.…”

Section: Linear-temporal Incremental Deployment (Ltid)mentioning

confidence: 99%

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Energy-Time Efficiency in Aerial Swarm Deployment

Stirling

Floreano

2013

Springer Tracts in Advanced Robotics

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A major challenge in swarm robotics is efficiently deploying robots into unknown environments, minimising energy and time costs. This is especially important with small aerial robots which have extremely limited flight autonomy. This paper compares three deployment strategies characterised by nominal computation, memory, communication and sensing requirements, and hence are suitable for flying robots. Energy consumption is decreased by reducing unnecessary flight following two premises: 1) exploiting environmental information gathered by the robots; 2) avoiding diminishing returns and reducing interference between robots. Using a 3-D dynamics simulator we examine energy and time metrics, and also scalability effects. Results indicate that a novel strategy that controls the density of flying robots is most promising in reducing swarm energy costs while maintaining rapid search times. Furthermore, we highlight the energy-time tradeoff and the importance of measuring both metrics, and also the significance of electronics power in calculating total energy consumption, even if it is small relative to locomotion power.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Deployment Strategiesmentioning

confidence: 99%

Section: Linear-temporal Incremental Deployment (Ltid)mentioning

confidence: 99%

See 2 more Smart Citations

Energy-Time Efficiency in Aerial Swarm Deployment

Stirling

Floreano

2013

Springer Tracts in Advanced Robotics

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“…The rangers can travel up to 20 km, greatly extending the search range of scouts. Chang et al [8] study the energy and time properties of different dispatching algorithms for ant-like robot systems. Yamaguchi [46] discusses adaptive formation control for mobile robots to keep their relative positions.…”

Section: Related Workmentioning

confidence: 99%

Deployment of mobile robots with energy and timing constraints

Mei

et al. 2006

IEEE Trans. Robot.

202

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Abstract-Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.

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Searching Energy-Efficient Route in Rough Terrain for Mobile Robot with Ant Algorithm

Anuntachai

Wongwirat

2012

Intelligent Robotics and Applications

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Energy-time-efficient adaptive dispatching algorithms for ant-like robot systems

Cited by 14 publications

References 14 publications

Energy-Time Efficiency in Aerial Swarm Deployment

Energy-Time Efficiency in Aerial Swarm Deployment

Deployment of mobile robots with energy and timing constraints

Searching Energy-Efficient Route in Rough Terrain for Mobile Robot with Ant Algorithm

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