Software Engineer Intern - Geolocalised intervention application development - Javascript
Stanley Robotics
Description de l'offre
Context :
At Stanley Robotics, we build a robotized parking solution that automatically stores cars, using a fleet of robots.
As we are deploying our solution to bigger sites, there is an increase in human-robot interactions. (e.g. for scheduled maintenance on vehicles)
To make these interactions safe and smooth, we want to integrate a geolocalised solution for humans to our product.
Your mission :
As an intern, you will be in charge of integrating a GPS RTK solution to our existing supervision application. You will also work on improving the adaptability of the application (web app) to mobile (Android), which has been initially designed for desktop usage.
This application has been developed in Javascript, using Vue.js framework.
You will join our R&D team of experienced developers from various backgrounds, in a challenging, creative and friendly atmosphere.
Profil recherché
Your profile :
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You are in your last year of engineering school - min 6 months internship
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Good programming skills in Javascript
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Some knowledge about Vue.js framework is a plus
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Some knowledge about Android ecosystem is a plus
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Be able to work autonomously on challenging tasks
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French and English at a professional level
Context :
At Stanley Robotics, we build a robotized parking solution that automatically stores cars, using a fleet of robots. In order to maximize the efficiency of our product, we need robots that can navigate smoothly in the yard, including in narrow areas, while surrounded by vehicles.
Your mission :
Join the Robotsoft team at Stanley Robotics, where you will work alongside our R&D team of experienced developers from various backgrounds, in a challenging, creative and friendly atmosphere. You will select one of the two key tracks to advance the field of robust robot navigation, with the shared objective of ensuring obstacle avoidance in narrow lanes while accounting for the robot’s operational and physical constraints.
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Track 1: Model-Free Navigation with Reinforcement Learning (RL):
You will implement and test new motion planning and control algorithms using RL techniques tailored for real-world applications. -
Track 2: Model Predictive Control (MPC) for Motion Planning and Control:
You will design and implement MPC-based navigation algorithms that account for the system’s limitations while operating within tight runtime constraints.
Responsibilities:
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Design, implement, and test motion planning and control algorithms in C++/Python
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Focus on obstacle avoidance in constrained environments, such as narrow lanes, while respecting the robot’s limitations (e.g., speed, acceleration, steering angle, etc.)
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Explore and implement state-of-the-art methods for robot navigation, including MPC- or RL-based techniques
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Document and present findings, including testing results and performance analysis