Software Engineer Intern - Implementing a software-based preventive maintenance approach - C++/Python
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.
In order to maximize the efficiency of our product, we need to anticipate and plan the maintenance operations. Hardware components may trigger alerts that must be processed smartly enough to identify which ones can be accepted (let’s call them “false positives”), and which ones require an intervention.
Your mission :
As an intern in the Robotsoft team, you will be in charge of deploying a hardware diagnostics module (some foundations of which have already been developed).
The objective will be, for each hardware component (sensors, actuators, …), to define the strategies that must be implemented to better anticipate hardware issues and alert operators automatically.
You will join our R&D team of experienced developers from various backgrounds, in a challenging, creative and friendly atmosphere.
Profil recherché
Your profile :
-
You are in your last year of engineering school - min 6 months internship
-
Good programming skills in C++
-
Experience in ROS, Linux, Git ecosystems
-
Some knowledge about CAN protocol is a plus
-
Be able to work autonomously on challenging tasks
-
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.
-
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:
-
Design, implement, and test motion planning and control algorithms in C++/Python
-
Focus on obstacle avoidance in constrained environments, such as narrow lanes, while respecting the robot’s limitations (e.g., speed, acceleration, steering angle, etc.)
-
Explore and implement state-of-the-art methods for robot navigation, including MPC- or RL-based techniques
-
Document and present findings, including testing results and performance analysis