A hands-off, multi-robot display for communicating situation awareness to operators

Crossman, Jacob; Marinier, Robert P.; Olson, Edwin

doi:10.1109/cts.2012.6261036

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…At the time, we modeled the uncertainty of odometry and loop closing edges with simple Gaussians, but despite extensive validation of these edges prior to optimization, some of these edges had large errors that were virtually impossible given their noise model. Even with a novel interface allowing a human to help untangle the resulting map [CMO12], errors were still evident (see Fig. 7).…”

Section: Introductionmentioning

confidence: 99%

Inference on networks of mixtures for robust robot mapping

Olson¹,

Agarwal²

2012

Robotics: Science and Systems VIII

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The central challenge in robotic mapping is obtaining reliable data associations (or "loop closures"): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure.We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being "right" or "wrong", we propose characterizing the error of those loop closures in a more expressive manner that can account for their non-Gaussian behavior. Our approach leads to an fully-integrated Bayesian framework for dealing with error-prone data. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance.We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the "front-end" loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world datasets that motivated this work.

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Section: Introductionmentioning

confidence: 99%

Inference on networks of mixtures for robust robot mapping

Olson¹,

Agarwal²

2012

Robotics: Science and Systems VIII

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“…Controlling robots through this type of OCU requires full attention by the operator. Operators typically use a joystick type controller to enable various motors on the robot to move it [5]. OCU devices can be illustrated as in Figure 3.…”

Section: B Ocumentioning

confidence: 99%

Graphical User Interface Optimalization Based C# with Visual Studio to Operator Control Unit Rhino Robot

Tamara¹,

Affandi²,

Arifin³

et al. 2019

IJPS

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System of communication and data storage on the old version Graphical User Interface (GUI) can not developed or modified because it is not made in a modular way. In addition, the memory capacity is quite large because there are XNA Framework function and excessive robot control menus. Based on these problems we need to optimize GUI on the Operator Control Unit (OCU) Rhino robot. Programming language using C # with Integrated Development Environment (IDE) Visual Studio. Communication systems and data storage robots are grouped into 3 parts of the program with inheritance class. It creation use one of the IDE features of the Windows Presentation Foundation (WPF). The result of this research is program of communication system and data storage more structured so that can be developed for further research. After that, the memory capacity of the new version of GUI is 57.4 MB with an average memory usage of about 513.5 MB and the number of GUI pages as many as 8 with 3 main page details and 5 supporting pages to control robot motion.

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“…There is, however, some work by Crossman et al ( 2012 ), Alonso-Mora et al ( 2015 ), and Dawson et al ( 2015 ) that does looks toward human interaction with autonomous planning systems. We see two fundamental and contrasting approaches that are taken.…”

Section: Related Workmentioning

confidence: 99%

Shaping of Shared Autonomous Solutions With Minimal Interaction

2018

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A fundamental problem in creating successful shared autonomy systems is enabling efficient specification of the problem for which an autonomous system can generate a solution. We present a general paradigm, Interactive Shared Solution Shaping (IS3), broadly applied to shared autonomous systems where a human can use their domain knowledge to interactively provide feedback during the autonomous planning process. We hypothesize that this interaction process can be optimized so that with minimal interaction, near-optimal solutions can be achieved. We examine this hypothesis in the space of resource-constrained mobile search and surveillance and show that without directly instructing a robot or complete communication of a believed target distribution, the human teammate is able to successfully shape the generation of an autonomous search route. This ability is demonstrated in three experiments that show (1) the IS3 approach can improve performance in that routes generated from interactions in general reduce the variance of the target detection performance, and increase overall target detection; (2) the entire IS3 autonomous route generation system's performance, including cost of interaction along with movement cost, experiences a tradeoff between performance vs. numbers of interactions that can be optimized; (3) the IS3 autonomous route generation system is able to perform within constraints by generating tours that stay under budget when executed by a real robot in a realistic field environment.

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A hands-off, multi-robot display for communicating situation awareness to operators

Cited by 12 publications

References 8 publications

Inference on networks of mixtures for robust robot mapping

Inference on networks of mixtures for robust robot mapping

Graphical User Interface Optimalization Based C# with Visual Studio to Operator Control Unit Rhino Robot

Shaping of Shared Autonomous Solutions With Minimal Interaction

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