Autonomous Robots Sharing a Charging Station with no Communication: a Case Study

Sempé, François; Muñoz, Antonio; Drogoul, Alexis

doi:10.1007/978-4-431-65941-9_10

Cited by 21 publications

(12 citation statements)

References 1 publication

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“…Previous studies have proposed to find the most energy-efficient path for robots to move to a certain location or to cover a large area [2,3,5,8,15,47,49]. Other solutions coordinate various AMRs to optimize their charging scheduling [6,20,21,23,24,34,[37][38][39]. Most of the above studies focus on the energy consumption of the robots due to the mechanical parts.…”

Section: Related Workmentioning

confidence: 99%

E2m

Liu

Chen

Brocanelli

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack for autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on HydraOne, a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a

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Section: Related Workmentioning

confidence: 99%

E2m

Liu

Chen

Brocanelli

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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“…This information was in fact very useful for defining better sharing strategies. For a detailed description of this work see Sempé et al (2002). A second example of representing the dynamics of systems in a multi-robot resource sharing scenario concerns the sharing of the environment.…”

Section: Microscopic Interactions In Small Group Sizementioning

confidence: 99%

“…1. Autonomy of three mobile robots sharing a charging station using a basic strategy with a 7.5-minute recharge threshold (a), and using an opportunistic strategy with a flexible recharge threshold (b) (Sempé et al, 2002).…”

Section: Microscopic Interactions In Small Group Sizementioning

confidence: 99%

On the Problem of Representing and Characterizing the Dynamics of Multi-Robot Systems

Muñoz-Meléndez¹

2011

Multi-Robot Systems, Trends and Development

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“…Autonomous energy management comprises several issues: the recharging of equipment, managing the docking approach, individual energy homeostasis and the behavioural strategy of the whole group (e.g. [9,10]). This article focuses on collective energy foraging, using an example implementation in a swarm of autonomous 'Jasmine' micro-robots [11].…”

Section: Introductionmentioning

confidence: 99%

Specialization and generalization of robot behaviour in swarm energy foraging

Kernbach

Nepomnyashchikh

Kancheva

et al. 2012

Mathematical and Computer Modelling of Dynamical Systems

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Energy supply is one of the most serious problems for micro-mechatronic devices. For collective systems, such as sensor networks or swarms of autonomous micro-robots, collective energy management is especially hard. This work describes a kinetic model of energy foraging and an application of bio-inspired harvesting behaviour to a real robot swarm. The heuristic strategy derived allows proper collective management of energy resources without using global knowledge and guarantees a good swarm efficiency. Despite the whole swarm having the same behavioural rules, some robots specialize in only a few foraging activities, whereas others are more universal in their behaviour. Such emergence of 'specialists' and 'generalists' is observed in animal groups and can indicate common behavioural principles underlying natural and artificial systems.

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Autonomous Robots Sharing a Charging Station with no Communication: a Case Study

Cited by 21 publications

References 1 publication

E2m

E2m

On the Problem of Representing and Characterizing the Dynamics of Multi-Robot Systems

Specialization and generalization of robot behaviour in swarm energy foraging

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