VizRob: Effective Visualizations to Debug Robotic Behaviors

Miguel Campusano; Alexandre Bergel

doi:10.1109/IRC.2019.00021

VizRob: Effective Visualizations to Debug Robotic Behaviors

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Building and debugging robotic programs is known to be difficult. The robotic community has produced numerous tools, APIs and middlewares to help debug and trace the construction and execution of robotic behaviors. However, mostof available debugging tools are text and log-oriented, leading toa tedious and ad-hoc debugging activity. In this paper we fully describe VizRob, a tool to debug robotic behaviors using logs and execution time. VizRob producesinteractive visualizations built from log traces within a state machine model, that is, the visual representation of the behavior. VizRob is founded on deficiencies we empirically found from semi-structured interviews and a revision of tutorial materials. A small case study received an initial feedback of VizRob in a robotic software engineering team. Our case study shows: (i) VizRob helps engineers solve intricate debugging scenarios and (ii) engineers perceive VizRob as filling a relevant gap within the current tools for building robotic behavior.

Original language	English
Title of host publication	Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019
Number of pages	8
Publisher	IEEE
Publication date	26. Mar 2019
Pages	86-93
Article number	8675684
ISBN (Electronic)	9781538692455
DOIs	https://doi.org/10.1109/IRC.2019.00021
Publication status	Published - 26. Mar 2019
Event	3rd IEEE International Conference on Robotic Computing, IRC 2019 - Naples, Italy Duration: 25. Feb 2019 → 27. Feb 2019

Conference

Conference	3rd IEEE International Conference on Robotic Computing, IRC 2019
Country/Territory	Italy
City	Naples
Period	25/02/2019 → 27/02/2019

Series	Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

nested state machines
robotic behaviors
visualizations

Documents & Links

10.1109/IRC.2019.00021

Cite this

@inproceedings{26931d19fa884941936bf5cf3cf61db3,

title = "VizRob: Effective Visualizations to Debug Robotic Behaviors",

abstract = "Building and debugging robotic programs is known to be difficult. The robotic community has produced numerous tools, APIs and middlewares to help debug and trace the construction and execution of robotic behaviors. However, mostof available debugging tools are text and log-oriented, leading toa tedious and ad-hoc debugging activity. In this paper we fully describe VizRob, a tool to debug robotic behaviors using logs and execution time. VizRob producesinteractive visualizations built from log traces within a state machine model, that is, the visual representation of the behavior. VizRob is founded on deficiencies we empirically found from semi-structured interviews and a revision of tutorial materials. A small case study received an initial feedback of VizRob in a robotic software engineering team. Our case study shows: (i) VizRob helps engineers solve intricate debugging scenarios and (ii) engineers perceive VizRob as filling a relevant gap within the current tools for building robotic behavior.",

keywords = "nested state machines, robotic behaviors, visualizations",

author = "Miguel Campusano and Alexandre Bergel",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd IEEE International Conference on Robotic Computing, IRC 2019 ; Conference date: 25-02-2019 Through 27-02-2019",

year = "2019",

month = mar,

day = "26",

doi = "10.1109/IRC.2019.00021",

language = "English",

series = "Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019",

pages = "86--93",

booktitle = "Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019",

publisher = "IEEE",

address = "United States",

}

Campusano, M & Bergel, A 2019, VizRob: Effective Visualizations to Debug Robotic Behaviors. in Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019., 8675684, IEEE, Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019, pp. 86-93, 3rd IEEE International Conference on Robotic Computing, IRC 2019, Naples, Italy, 25/02/2019. https://doi.org/10.1109/IRC.2019.00021

VizRob : Effective Visualizations to Debug Robotic Behaviors. / Campusano, Miguel; Bergel, Alexandre.

Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019. IEEE, 2019. p. 86-93 8675684 (Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Bergel, Alexandre

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AB - Building and debugging robotic programs is known to be difficult. The robotic community has produced numerous tools, APIs and middlewares to help debug and trace the construction and execution of robotic behaviors. However, mostof available debugging tools are text and log-oriented, leading toa tedious and ad-hoc debugging activity. In this paper we fully describe VizRob, a tool to debug robotic behaviors using logs and execution time. VizRob producesinteractive visualizations built from log traces within a state machine model, that is, the visual representation of the behavior. VizRob is founded on deficiencies we empirically found from semi-structured interviews and a revision of tutorial materials. A small case study received an initial feedback of VizRob in a robotic software engineering team. Our case study shows: (i) VizRob helps engineers solve intricate debugging scenarios and (ii) engineers perceive VizRob as filling a relevant gap within the current tools for building robotic behavior.

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VizRob: Effective Visualizations to Debug Robotic Behaviors

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