A fuzzy approach to the autonomous recharging problem for mobile robots

Siqueira, Fernando de Lucca; Plentz, Patricia Della Mea; Pieri, Edson Roberto De

doi:10.1109/fskd.2016.7603326

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The conditions are, present level of energy remaining, the target distance to reach to perfoand distance to be travelled to reach energy station. The results have shown improvement in the overall performance of the robot recharging mechanism without wasting much of the time and energy [10].…”

Section: Related Workmentioning

confidence: 97%

Sensor Guided Docking of Autonomous Mobile Robot for Battery Recharging

Rao*¹,

Shivakumar²

2019

IJRTE

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For uninterrupted working of autonomous mobile robot, battery level needs to be continuously monitored. Normally, few hours of peak usage may be provided by rechargeable batteries. The batteries need to be recharged when its voltage reaches threshold value. The autonomous mobile robot docking for recharging is implemented in this paper. The robot employs sensors, microcontroller ATMEGA16 and voltage divider circuit. The robot has two sensors, IR sensor and Ultrasonic sensor which finds the obstacle (docking station) and orient the robot in the proper position for docking and recharging. The discharging voltage of the battery is detected using voltage divider circuit. The robot’s ultrasonic and IR sensors perform the detection of docking station for battery recharging. Ultrasonic sensor helps to detect the obstacle and range from robot.IR sensor is used for the orientation of the robot at the exact position for docking purpose. These two sensors in the robot provide the data about the distance from the docking station to the microcontroller. The algorithm called as Random search algorithm (RAS) is developed to assist the robot to get docked for recharging. This algorithm randomly moves the robot to search the docking station, if found will move towards it and starts for docking and charging or else it rotates to the right. Atmel studio 6 is the software development environment used to write code in Embedded C language.

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Section: Related Workmentioning

confidence: 97%

Sensor Guided Docking of Autonomous Mobile Robot for Battery Recharging

Rao*¹,

Shivakumar²

2019

IJRTE

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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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“…The first deals with designing and testing the hardware and software employed to help the robot maneuver to the charging station. The second involves deciding when the robot should go to the charging station ( de Lucca Siqueira, Della Mea Plentz & De Pieri, 2016 ). This work focuses only on the second problem.…”

Section: Introductionmentioning

confidence: 99%

“…Based on a fuzzy inference system ( de Lucca Siqueira, Della Mea Plentz & De Pieri, 2016 ) propose a solution for the first sub-problem. It considered three crisp input variables, the battery level, the distance to the charging station, and the distance to the destination point for the fuzzy mapping rules.…”

Section: Introductionmentioning

confidence: 99%

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

López-Lozada

Rubio-Espino

Azuela

et al. 2021

PeerJ Computer Science

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Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

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A fuzzy approach to the autonomous recharging problem for mobile robots

Cited by 5 publications

References 6 publications

Sensor Guided Docking of Autonomous Mobile Robot for Battery Recharging

Sensor Guided Docking of Autonomous Mobile Robot for Battery Recharging

E2m

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

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