157 lines
7.7 KiB
Markdown
157 lines
7.7 KiB
Markdown
file:: [RoSE2024_paper_4_1704401083677_0.pdf](../assets/RoSE2024_paper_4_1704401083677_0.pdf)
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file-path:: ../assets/RoSE2024_paper_4_1704401083677_0.pdf
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- The growing complexity of work cells necessitates the development of improved techniques, methodologies, and tools for their creation, optimization, and debugging.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 659d4089-7078-43e8-998b-6dc36ec83968
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hl-stamp:: 1704804492761
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- application of dynamic visualizations for the debugging process using domainspecific knowledge
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 659d4094-be64-4958-9a18-7e3a4fb19edb
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hl-stamp:: 1704804502339
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- These visualizations are tailored for debugging collaborative robots, focusing on pick-and-place applications, and are integrated into a proof-of-concept tool.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 659d40be-c5e8-4aa6-be74-a2182a4e9d5c
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- we showcase its ability to enable operators to verify the correctness of the robot’s behavior and identify program failures using several case studies.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 659d40d6-ef97-427b-b234-4b08df4ba442
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- While the figure tends to vary, a general agreement exists that 60% to 80% of a system’s budget is spent on software maintenance [4].
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 659d4137-6198-4c9e-b921-a491cca39e06
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hl-stamp:: 1704804666840
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- despite the established significance of debugging practices in software engineering, their integration into the robotics domain still needs to be explored.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 659d4e2c-ed4c-4a6d-b94d-bc291d0ece18
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hl-stamp:: 1704807982914
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- In this study, we introduce a visualization tool designed for debugging collaborative robots, specifically addressing common challenges in pick-and-place applications involving robotic arms.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 659d4e57-4484-4ce8-9a4d-5f1f160634f9
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- The tool presented in this paper differs from previous approaches by presenting runtime information in state resolution, intricately linking the program and its contents to execution. Consequently, this facilitates the presentation of visualizations and pertinent debugging metrics within a more contextually enriched execution framework
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ls-type:: annotation
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hl-page:: 1
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hl-color:: yellow
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id:: 659d4ed4-1175-4b5e-84d8-1af0a9cc3aa5
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hl-stamp:: 1704808152918
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- facilitating the visualization of diverse sensor and state data from robots, commonly used in the pre-deployment phase.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 659d4f50-a944-4126-a1a6-9d5d03e5aa3f
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hl-stamp:: 1704808275093
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- the visualizations crafted in our tool apply to cobots operating outside of the ROS domain.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: yellow
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id:: 659d4f62-598c-4e63-a9cc-dfecd1be58e8
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hl-stamp:: 1704808294137
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- takes this concept a step further by enabling for monitoring strategies, i.e., configuring alarm systems to reduce unforeseen halts.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 659d4fbb-a901-482d-b8bc-d0781432cf18
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- izRob lacks the seamless integration between event information and runtime
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 659d4fea-d2c4-444e-b04c-be713be0a889
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hl-stamp:: 1704808429007
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- various initiatives explore the application of augmented or virtual reality (AR or VR) for visualizing data to comprehend robot behaviors
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 659d4ff7-7537-41e1-9c7e-a5fef572fd45
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- RELATED WORK
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ls-type:: annotation
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hl-page:: 1
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id:: 659d5029-2949-420a-a88a-e088de50b2fa
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hl-stamp:: 1704808494093
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- Even with partial grasping, the behavior is correct, though potentially less desirable for operators due to reductions in the vacuum gripper’s effectiveness.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 659d5106-ca2c-422b-8dad-1fa785fa0417
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hl-stamp:: 1704808712378
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- Operators armed with comprehensive information can understand and adjust the arm’s behavior. However, current cobot application evaluations rely on continuous visual inspection, and new deployments are typically tested across an entire work shift.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 659d5125-48e7-43ac-bb93-1d1d93e808c5
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- Our visualizations utilize data from the cobot and its environment, enabling operators to assess failures remotely
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 659d514e-eac6-4219-b31c-50bb5d4ac425
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hl-stamp:: 1704808832318
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- rrors were detected in 40% of the recorded cycles
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ls-type:: annotation
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hl-page:: 3
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hl-color:: yellow
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id:: 659d5274-92f5-47e9-9bee-74d420763c28
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- triangular figures
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 659d52f8-60ef-47d5-a252-8dcf7dad2498
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hl-stamp:: 1704809210053
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- previous
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 659d52fc-6517-4adc-a807-d29e7fd9542b
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- circles
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ls-type:: annotation
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hl-page:: 3
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hl-color:: blue
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id:: 659d5301-4165-4086-bd82-1395d8f7b5cb
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hl-stamp:: 1704809219535
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- current
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ls-type:: annotation
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hl-page:: 3
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hl-color:: blue
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id:: 659d5306-f7ef-49c3-af67-9c9b32fface5
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- squares
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 659d5309-0170-4fd6-b159-4000715eebb7
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hl-stamp:: 1704809228064
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- following
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 659d530d-3a9a-4b98-aa94-82c4c1eea3c6
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- future
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 659d530e-a576-4046-abb3-13ff836a6499
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- The tool incorporates multiple visualizations that aid users in discerning the success or failure of the robotic arm in picking up objects
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 659d5357-6d24-4f33-9ab3-db096aeeb2b6
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- we demonstrate the tool’s efficacy in enabling users to comprehend the behavior of robotic arms
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 659d535e-4d88-4076-b370-7a5315a5fb15
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- 412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464raw variable names Mouse-hover shows values at step Both suction cup signals above configured target One suction cup signal above target One suction cup signal below target Both suction cup signals below target Controller steps Percentage of gripper max vacuum level(b) Partial (incorrect) object grasping(c) Failed object grasping(a) Successful object grasping(d) Variable overview and step selector Values of variables created in script Note, these variables were also used for the visualizations Values at selected step Figure 3: Vacuum level visualization for success, fail and partial grasping. Explanatory text in red. emphasizing pick-and-place applications. The tool incorporates multiple visualizations that aid users in discerning the success or failure of the robotic arm in picking up objects. Through an experimental setup, we demonstrate the tool’s efficacy in enabling users to comprehend the behavior of robotic arms. Users can swiftly assess whether the robotic arm aligns with the intended behavior specified in the source code. However, it is essential to note that our findings are indicative, and further validation through user involvement is imperative to substantiate the tool’s effectiveness in cobot applications debugging. We aim to explore avenues for extending this tool to debug a broader spectrum of cobot applications effectively. Therefore, in future work, our focus will entail a comprehensive analysis of diverse cobot applications, en
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 659d5369-b6e3-41fe-afa6-f32db23f4543 |