Wheelly Session Pipeline

This batch script automates the Wheelly session workflow, from robot session execution and KPI collection to final report generation.

Overview

The pipeline performs the following stages:

1. Load the execution configuration.
2. Clean previous execution outputs.
3. Run a Wheelly robot session.
4. Generate report data.
5. Create the final report using GNU Octave.

The workflow ensures that each execution starts from a clean output state by removing previous KPI, CSV, and report directories.

Prerequisites

Before running the script, ensure that:

• The project has been built.
• Java is installed and available.
• GNU Octave is installed.
• The configuration script bin/setConfig.cmd is correctly configured.
• All required environment variables are defined.
• The Wheelly application dependencies are available.

Configuration Variables

The pipeline uses the following configuration variables, initialized by:

bin\setConfig.cmd

Variable	Description
INFERENCE	Output directory containing inference data generated by the Wheelly session.
KPIS	Output directory containing session metrics and KPIs.
CSVS	Output directory containing CSV report data.
REPORT	Output directory containing generated reports.

Pipeline Stages

1. Load Configuration

The script starts by loading all required configuration variables.

call bin\setConfig.cmd

2. Clean Previous Results

Before starting a new session, previous execution artifacts are removed.

rmdir /S /Q %KPIS%
rmdir /S /Q %CSVS%
rmdir /S /Q %REPORT%

The following directories are cleared:

Directory	Content
KPIS	Session metrics and KPI files.
CSVS	CSV files generated for reporting.
REPORT	Previously generated reports.

3. Run Wheelly Session

The main Wheelly application is executed using:

org.mmarini.wheelly.apps.Wheelly

Command:

call bin\runnit.cmd org.mmarini.wheelly.apps.Wheelly ^
    -k %KPIS% ^
    -w ^

Parameters:

Option	Description
-k	KPI output directory.
-w	Enable multi-window panels.

The execution runs the Wheelly robot session, generates inference data in INFERENCE, and stores KPI metrics in KPIS.

4. Generate Report Data

After the session completes, the reporting utility extracts the metrics and generates CSV files.

call bin\report.cmd -p %KPIS% %CSVS%

The generated files are stored in:

CSVS

5. Generate Final Report

The final report is created using GNU Octave.

octave/dlReport.m

The script reads the generated CSV files and produces the final report.

Generated Artifacts

After a successful execution, the following artifacts are available:

• Generated inference data (INFERENCE)
• Session KPI data (KPIS)
• Report CSV files (CSVS)
• Final report generated by octave/dlReport.m

Execution Flow

The following diagram illustrates the sequence of operations performed by the pipeline.

flowchart TD

    START([Start])

    CFG["Load Configuration"]

    CLEAN["Remove Previous Results"]

    SESSION["Run Wheelly Session"]

    REPORT["Generate Report Data"]

    OCTAVE["Run dlReport.m"]

    END([End])

    START --> CFG
    CFG --> CLEAN
    CLEAN --> SESSION
    SESSION --> REPORT
    REPORT --> OCTAVE
    OCTAVE --> END

Data Flow

The following diagram illustrates how data and generated artifacts move through the pipeline.

flowchart LR

    INF[(Generated Inference Data)]

    KPI[(Session KPIs)]

    CSV[(CSV Reports)]

    REP[(Final Report)]

    WHEELY["Wheelly Application"]

    RPT["report.cmd"]

    OCT["dlReport.m"]

    WHEELY --> INF
    WHEELY --> KPI

    KPI --> RPT
    RPT --> CSV

    CSV --> OCT
    OCT --> REP

Release

Current release: 0.26.0