A Distributed Range-Only Collision Avoidance Approach for Low-cost Large-scale Multi-Robot Systems

Han, Ruihua; Chen, Shengduo; Hao, Qi

doi:10.1109/iros45743.2020.9341539

Cited by 7 publications

(13 citation statements)

References 22 publications

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“…The key feature of HIL is that all algorithms must be tested in close-to-reality simulators or real-world environments so as to make re-optimization possible [15]. For simulation, we adopt Gazebo [10], [12], a high-fidelity robotic evaluation platform that adopts Open Dynamics Engine for motion generation of the robots and Open Graphics Library Engine for the visualization of the world. On top of the two engines, Gazebo realizes each physical object as a model that is composed of rigid bodies, joints, sensors, and interfaces for client programs to control the model.…”

Section: Hardware-in-the-loop For Re-optimizationmentioning

confidence: 99%

“…A mobile battery powers the chassis, sensor, computer, and the transmitter. Communication among these devices is implemented via ROS, which is a distributed communication framework supporting integrative and heterogenous systems [12]. In ROS, all processes that perform computations are implemented as nodes, where a master node controls the global system and large-scale slave nodes manage programs on each device.…”

Section: Hardware-in-the-loop For Re-optimizationmentioning

confidence: 99%

“…This is because each robot can only estimate the positions and velocities of other robots but cannot know their reactions and intents. In this case, each robot needs to select a velocity outside the reciprocal velocity obstacle (RVO) region induced by other robots [12]. It has been proved that the RVO provides a sufficient and necessary condition for a robot to avoid collisions with an obstacle moving with a known velocity but unknown intention [10].…”

Section: Local Planning For Collision Avoidancementioning

confidence: 99%

“…Therefore, the UAV WET is suitable for time-sensitive applications, while the robotic WET is suitable for energysensitive applications. Existing algorithms for robotic WET can be categorized into two types: global planning algorithms [4]- [6] and local planning algorithms [10]- [12]. Global planning algorithms [4]- [7] determine the anchor points, routes, and resources (e.g., charging time and beam directions at each anchor point), while local planning algorithms [10]- [12] periodically adjust the route for collision avoidance in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

“…Existing algorithms for robotic WET can be categorized into two types: global planning algorithms [4]- [6] and local planning algorithms [10]- [12]. Global planning algorithms [4]- [7] determine the anchor points, routes, and resources (e.g., charging time and beam directions at each anchor point), while local planning algorithms [10]- [12] periodically adjust the route for collision avoidance in a dynamic environment. These global and local planning algorithms have been studied separately for robotic WET systems.…”

Section: Introductionmentioning

confidence: 99%

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Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation

Wang¹,

Ren²,

Hong³

et al. 2022

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Wireless energy transfer (WET) is a groundbreaking technology for cutting the last wire between mobile sensors and power grids in smart cities. Yet, WET only offers effective transmission of energy over a short distance. Robotic WET is an emerging paradigm that mounts the energy transmitter on a mobile robot and navigates the robot through different regions in a large area to charge remote energy harvesters. However, it is challenging to determine the robotic charging strategy in an unknown and dynamic environment due to the uncertainty of obstacles. This paper proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: 1) efficient model updates and re-optimization based on the lastround experimental data; 2) iterative refinement of the anchor list for adaptation to different environments; 3) verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves the WET mission completion time while satisfying collision avoidance and energy harvesting constraints.

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Section: Hardware-in-the-loop For Re-optimizationmentioning

confidence: 99%

Section: Hardware-in-the-loop For Re-optimizationmentioning

confidence: 99%