UB robot swarm — Design, implementation, and power management

Patil, Madhav; Abukhalil, Tamer; Patel, Sarosh; Sobh, Tarek M.

doi:10.1109/icca.2016.7505339

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…A complete discussion about the robotic agents used in our experiments can be found in [25]. The robotic platforms shown in Figure 6 are built using Arduino UNO, Arduino Due, and Digilent PIC boards.…”

Section: Resultsmentioning

confidence: 99%

Deployment Environment for a Swarm of Heterogeneous Robots

et al. 2016

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Abstract:The objective of this work is to develop a framework that can deploy and provide coordination between multiple heterogeneous agents when a swarm robotic system adopts a decentralized approach; each robot evaluates its relative rank among the other robots in terms of travel distance and cost to the goal. Accordingly, robots are allocated to the sub-tasks for which they have the highest rank (utility). This paper provides an analysis of existing swarm control environments and proposes a software environment that facilitates a rapid deployment of multiple robotic agents. The framework (UBSwarm) exploits our utility-based task allocation algorithm. UBSwarm configures these robots and assigns the group of robots a particular task from a set of available tasks. Two major tasks have been introduced that show the performance of a robotic group. This robotic group is composed of heterogeneous agents. In the results, a premature example that has prior knowledge about the experiment shows whether or not the robots are able to accomplish the task.

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Section: Resultsmentioning

confidence: 99%

Deployment Environment for a Swarm of Heterogeneous Robots

et al. 2016

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“…In such systems, a control circuit is also required to protect the battery-life during charging time because Li-Po cells are very sensitive during the charging phase. e microcontroller integrated on the battery is responsible to provide a control to regulate the charging process [22][23][24].…”

Section: Power Optimization Using Heuristic Functionmentioning

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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“…In this section, validation experiments on an actual robot system (e.g., [26]), a PatrolBot that is equipped with a Microsoft Kinect Sensor V2 and a laser (Sick LMS 200), are conducted to show the benefit of integration of human trajectory prediction. The Kinect V2 is used to recognize the pedestrians via the skeletal tracking algorithm [19].…”

Section: Validation Experiments With a Real Low-density Crowdmentioning

confidence: 99%

Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes

et al. 2018

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For a mobile robot, navigation skills that are safe, efficient, and socially compliant in crowded, dynamic environments are essential. This is a particularly challenging problem as it requires the robot to accurately predict pedestrians' movements, analyse developing traffic situations, and plan its own path or trajectory accordingly. Previous approaches still exhibit low accuracy for pedestrian trajectory prediction, and they are prone to generate infeasible trajectories under complex crowded conditions. In this paper, we develop an improved socially conscious model to learn and predict a pedestrian's future trajectory. To generate more efficient and safer trajectories in a changing crowed space, an online path planning algorithm considering pedestrians' predicted movements and the feasibility of the candidate trajectories is proposed. Then, multiple traffic states are defined to guide the robot finding the optimal navigation strategies under changing traffic situations in a crowded area. We have demonstrated the performance of our approach outperforms state-of-the-art approaches with public datasets, in low-density and simulated medium-density crowded scenarios. and then predicted the position and velocity of humans for a finite horizon. Schulz et al. [6] combined pedestrian intention recognition with path prediction, through an interacting multiple model filter in combination with a latent-dynamic conditional random field model. However, most of these research works are limited by independent models that fail to capture the complex interactions between the humans in the crowd. An alternative and recent method utilizes learning techniques to model the joint distribution of future trajectories of interacting agents based on a spatially local interaction model. Trautman et al. [7] proposed an interactive Gaussian process approach, whose kernels were used to model human dynamics, to capture cooperative collision avoidance between humans and a robot. Alahi et al. [8] proposed long-short term memory (LSTM) networks with "social" pooling layers, which learned general human movements and predicted their future trajectories. Recently, we proposed a socially conscious model considering the added features that affect the pedestrian's future trajectory, such as the walking direction of other pedestrians [9]. However, these approaches do not carefully distinguish the effects of a pedestrian's own history trajectory, and that of others, to the pedestrian's future trajectory, and this may hinder the improvement of model accuracy.Developing efficient online path planning algorithms to generate robots' safe and smooth trajectory is a basic problem in robot navigation. Safe trajectory generation is a very active field in mobile robotics and there are many recent contributions. Ravankar [10] and David [11] reviewed the state-of-the-art in smooth trajectory generation with comparisons in terms of kinematic feasibility and safe path generation recently. Additionally, the approaches that are available with robot operating system ...

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UB robot swarm — Design, implementation, and power management

Cited by 17 publications

References 16 publications

Deployment Environment for a Swarm of Heterogeneous Robots

Deployment Environment for a Swarm of Heterogeneous Robots

Power Optimization in Mobile Robots Using a Real-Time Heuristic

Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes

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