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Gautam, Avinash; Jha, Bhargav; Kumar, Gourav; Murthy, J. Krishna; Ram, Shri; Mohan, Sudeept

doi:10.1007/s10846-016-0416-2

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Many multi-robot coordination problems such as coverage (Cortes et al, 2004), mapping (Tapus and Siegwart 2005; Schmuck and Chli 2017; Se et al, 2001; Fenwick et al, 2002), frontier exploration (Gautam et al, 2017; Solanas and García 2004; Fox et al, 2006; Burgard et al, 2000), and persistent surveillance (Nigam et al, 2012; Stump and Michael 2011) rely on knowledge of relative positions and other state information between collaborating robots. Although on-board GPS devices or other sensors such as camera and LiDAR provide information about inter-agent relative positions and the environment and are commonly used for such problems, they have several limitations.…”

Section: Related Workmentioning

confidence: 99%

A wireless signal-based sensing framework for robotics

Jadhav

Wang

Zhang

et al. 2022

The International Journal of Robotics Research

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In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.

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Section: Related Workmentioning

confidence: 99%

A wireless signal-based sensing framework for robotics

Jadhav

Wang

Zhang

et al. 2022

The International Journal of Robotics Research

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“…all surface areas of interest need to be operated on. Coverage Path Planning (CPP) [7,8,9,10] is the task of determining a path that passes through all points of an area or volume of interest, so as to achieve complete coverage. For example, Fig.…”

Section: Introductionmentioning

confidence: 99%

Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements

Hassan

Liu

Paul

2017

J Intell Robot Syst

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Multiple autonomous industrial robots can be of great use in manufacturing applications, particularly if the environment is unstructured and custom manufacturing is required. Autonomous robots that are equipped with manipulators can collaborate to carry out manufacturing tasks such as surface preparation by means of grit-blasting, surface coating or spray painting, all of which require complete surface coverage. However, as part of the collaboration process, appropriate base placements relative to the environment and the target object need to be determined by the robots. The problem of finding appropriate base placements is further complicated when the object under consideration is large and has a complex geometric shape, and thus the robots need to operate from a number of base placements in order to obtain complete coverage of the entire object. To address this problem, an approach for Optimization of Multiple Base Placements (OMBP) for each robot is proposed in this paper. The approach aims to optimize base placements for multi-robot collaboration by taking into account task-specific objectives such as makespan, fair workload division amongst the robots, and coverage percentage, and manipulator-related objectives such as torque and manipulability measure. In addition, the constraint of robots maintaining an appropriate distance between each other and relative to the environment is taken into account. Simulated and real-world experiments are carried out to demonstrate the effectiveness of the approach and to verify that the simulated results are accurate and reliable.

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Autonomous robotic exploration based on frontier point optimization and multistep path planning

2019

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Autonomous robotic exploration of an unknown environment is a key technology for robot intelligence. In order to improve the efficiency of it, we propose a strategy based on frontier point optimization and multistep path planning in this paper. In the frontier points' optimization section, we present a random frontier points' optimization (RFPO) algorithm to select the frontier point with the highest evaluation value as the target frontier point. The evaluation function of frontier points is defined by considering information gain, navigation cost, and the precision of the localization of the robots. In the path planning section, we propose a multistep exploration strategy. Instead of planning the global path from the current position of the robot to the target frontier point directly, we set a local exploration path step size. When the robot's movement distance reaches the local exploration path step size, we reselect the current optimal frontier point for path planning to reduce the possibility that the robot may take some repetitive paths. Finally, the relevant experiments are carried out to verify the effectiveness of this strategy.INDEX TERMS Autonomous exploration, random frontier points optimization algorithm, frontier point evaluation function, multistep path planning.

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Cited by 8 publications

References 24 publications

A wireless signal-based sensing framework for robotics

A wireless signal-based sensing framework for robotics

Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements

Autonomous robotic exploration based on frontier point optimization and multistep path planning

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