Abstract

Software for robotic systems is becoming progressively more complex despite the existence of established software ecosystems like ROS, as the problems we delegate to robots become more and more challenging. Ensuring that the software works as intended is a crucial (but not trivial) task, although proper quality assurance processes are rarely seen in the open-source robotics community. This paper explains how we analyzed and improved a specialized path planner for steep-slope vineyards regarding its software dependability. The analysis revealed previously unknown bugs in the system, with a relatively low property specification effort. We argue that the benefits of similar quality assurance processes far outweigh the costs and should be more widespread in the robotics domain.

Highlights

  • The sector of agriculture has been changing over the years in response to the world’s demands

  • We show the benefits of roboticists and software engineers joining forces

  • This paper reports on the joint effort between roboticists and software engineers to improve the dependability of robotic software

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Summary

Introduction

The sector of agriculture has been changing over the years in response to the world’s demands. The terrains’ harsh and unstructured environments present several challenges, especially in the steep slope vineyards located in the Douro Demarcated Region, a UNESCO Heritage place These vineyards are unique, in that they grow along hills that block Global Navigation Satellite System (GNSS) signals and contain strong slopes, dangerous for robot navigation (see Figure 1). Our main contribution is a case study on improving the software dependability of AgRobPP, where we define a software analysis workflow that addresses various software development problems. This workflow is highly based on HAROS (https://github.com/g it-afsantos/haros, accessed on 22 August 2021) [11], a framework designed to analyze ROS software.

Related Work
Case Study Subject
Analysis Tools and Workflow
Code Quality Analysis
System Behavior Analysis
Conclusions
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