I am a Robotics and Industrial Engineer focused on industrial robotics, autonomous systems, robot simulation and intelligent automation.

My work combines robotics, software and industrial automation to develop and evaluate real-world engineering solutions, from ABB RobotStudio and PLC-integrated automation cells to autonomous mobile robot navigation, deep reinforcement learning and intelligent robot scheduling.

ABB RobotStudio · RAPID · CODESYS · Ladder Diagram · OPC UA · Industrial Automation

Industrial robotic pick-and-place cell integrating an ABB robot simulation with CODESYS PLC control through an OPC UA-based virtual automation architecture.

The project includes:

• PLC-controlled automatic and manual robot operation
• Deterministic PLC ↔ robot job handshaking
• ABB RAPID job dispatch and motion execution
• Dynamic pick-position selection from conveyor sensors
• Magnetic end-effector control
• Reusable CODESYS Ladder Diagram function blocks
• PLCopenXML source export for long-term inspection and portability
• Custom RobotStudio simulation assets and I/O engineering

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A* Path Planning · Particle Filter Localization · Simulated LiDAR · PyBullet · Python

Autonomous mobile robot simulation combining global path planning, probabilistic localization, waypoint tracking and dynamic obstacle handling.

The robot:

• Plans multi-goal missions using A*
• Estimates its pose using a Particle Filter
• Combines odometry with simulated LiDAR
• Navigates using the estimated pose rather than ground truth
• Detects dynamic workers and stops when safety distances are violated
• Generates navigation logs and quantitative performance metrics

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Proximal Policy Optimization · Stable-Baselines3 · PyTorch · Computer Vision · Python

Visual deep reinforcement learning agent trained with Proximal Policy Optimization (PPO) to navigate Super Mario Bros World 1-1.

The project implements:

• Visual preprocessing from RGB to 84 × 84 grayscale observations
• Temporal state representation using 4-frame stacking
• Frame skipping for more efficient interaction with the environment
• Custom reward shaping based on progress, enemies, coins and level completion
• Parallel training using 16 vectorized environments
• Observation and reward normalization with VecNormalize
• Periodic deterministic evaluation and checkpoint selection
• Training over 1,000,000 global timesteps
• Best deterministic evaluation reward of 4,876.60 at 900,000 timesteps

The repository includes the complete experiment notebook, trained PPO checkpoint, normalization state, quantitative results and gameplay recording.

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Monte Carlo Tree Search · Human-Robot Collaboration · ABB RobotStudio · Python

Master's thesis investigating intelligent online robot scheduling using Monte Carlo Tree Search (MCTS) in a simulated human-robot collaborative manufacturing environment.

The system integrates ABB RobotStudio with a Python-based decision-making layer and evaluates MCTS against conventional FIFO scheduling.

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Industrial Robotics · Robotic Additive Manufacturing · ABB RobotStudio · Experimental Validation

Bachelor's thesis developed in collaboration with ABB Robotics and RISE, focused on standardized testing and process optimization for large-scale robotic additive manufacturing.

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Robotics

• Industrial Robotics
• Autonomous Mobile Robots
• Robot Simulation
• Path Planning
• Localization
• Human-Robot Collaboration

Automation

• PLC Programming
• CODESYS
• Industrial Automation
• Robot / PLC Integration
• OPC UA
• Virtual Commissioning

Artificial Intelligence

• Reinforcement Learning
• Deep Reinforcement Learning
• Monte Carlo Tree Search
• Autonomous Decision-Making
• Computer Vision

Software

• Python
• C++
• ROS
• PyBullet
• MATLAB
• PyTorch

Robotics Tools

• ABB RobotStudio
• RAPID
• Digital Twin & Simulation

• M.Sc. — Intelligent Automation / Virtual Product Realization
  University of Skövde, Sweden

• M.Eng. — Industry & Robotics
  ESILV, France

• B.Sc. — Industrial Engineering
  University of Skövde, Sweden

• Bachelor's Degree — Robotics Engineering
  University of Alicante, Spain

📧 pablo.nietopareja02@gmail.com

Building intelligent robotic systems from simulation and algorithms to industrial automation.