Rutwik Bonde
Robotics Motion Planning Engineer
I am currently working as a Robotics Engineer (Motion Planning II) at ArcBest, where I architect and develop the navigation stack for autonomous forklifts. I hold a Master’s degree in Robotics from Worcester Polytechnic Institute. My work focuses on semantic-aware navigation, classical and learning-based motion planning, optimizing path planning strategies, and deploying autonomous systems in real-world warehouse environments. My expertise includes C++, Python, ROS1/ROS2, classical motion planning algorithms, learning-based planning, behavior planning, and simulation tools such as RViz. My interests lie in robot motion planning, path planning, behavior planning and computational geometry. I am passionate about building intelligent robotic systems that are efficient, scalable, and reliable, combining AI, optimization, and robotics to solve complex automation challenges.
Experience
ArcBest Technologies — Robotics Engineer II (Motion Planning)
April 2025 - Present | Arkansas, USA
- Leading the architectural redesign and migration of the global planning navigation stack from ROS1 to ROS2 to improve scalability, modularity, and real-time performance across multiple autonomous warehouse vehicle platforms.
- Architected and developed an Auto-Guidance Goal Map Generation system which automatically generates and validates navigation goal locations based on semantic data across large warehouse maps for cloud fleet managers, reducing manual map configuration effort.
- Improving global path planner robustness and runtime efficiency through mission manipulation, algorithmic optimization, advanced heuristics, and improved semantic reasoning for large warehouse environments.
- Architected a large-scale Multi-Robot Path Planning framework to coordinate fleets of autonomous forklifts operating in dense and dynamic warehouse environments while avoiding congestion and optimizing fleet throughput.
ArcBest Technologies — Robotics Engineer I (Path Planning)
June 2023 - April 2025 | Arkansas, USA
- Developed core components of the autonomous navigation stack in C++ for industrial warehouse vehicles including global path planning, obstacle avoidance, behavior planning, and speed planning modules.
- Led the research and development of a discrete path planner (customized hybrid A* algorithm) from scratch for a non-holonomic robot, integrating semantic map data into its evaluation functions.
- Implemented behavior tree modules for autonomous recovery behaviors, enabling robots to handle obstacle blocked paths and navigation failures.
- Optimized local motion planning algorithms including ARA* based replanning and improved trajectory following using a Pure Pursuit controller for stable and smooth vehicle motion.
- Successfully deployed and validated the navigation stack across three customer warehouse facilities supporting real-world autonomous logistics operations.
- Built automated testing pipelines using ROS tests and Google Test (GTest) to verify path planning algorithms and validate robot behaviors on physical robotic systems.
ArcBest Technologies — Robotics Software Intern
Jan 2023 - May 2023 | Arkansas, USA
- Contributed to development of behavior planning and path planning components for autonomous mobile robots operating in warehouse logistics environments.
- Developed simulation workflows and designed a virtual warehouse testing environment in Webots to evaluate robot navigation strategies before deployment on physical robotic platforms.
- Assisted in debugging and validating navigation algorithms through simulation-based testing and performance evaluation.
Nanyang Technological University — Research Intern
Aug 2020 - Dec 2020 | Singapore
- Conducted research on diagonalization algorithms for solving the classical Art Gallery Problem in computational geometry.
- Implemented algorithmic solutions in Python to determine optimal guard placement strategies for polygonal environments.
- Applied computational geometry methods including Voronoi diagrams, Delaunay triangulations, and polygon skeletonization to analyze spatial coverage and guard placement efficiency.
- Evaluated algorithm performance across multiple geometric test cases and benchmark environments.
Projects
Semantic Global Planner
Custom C++ global planner integrating semantic zones such as egress areas, fire lanes, and pedestrian regions for AMR navigation.
Multi-Robot Path Planning
Algorithms for coordinated motion planning among multiple robots with collision avoidance and optimization-based control.
Path Planning Visualization
Interactive grid visualization demonstrating search algorithms like A*, Dijkstra, and BFS.
Skills
Programming Languages
- C++
- Python
- C
Software Tools
- ROS1 & ROS2
- Matlab
- Linux
- Docker
- GitHub
- Bitbucket
Libraries
- Numpy
- Pytorch
- Matplotlib
Robotics
- Motion Planning
- Robot Routing
- Learning-Based Planning
- Behavior Trees
- Behavior Planning
Certifications
Computational Geometry Research Internship - NTU Singapore
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Neural Networks and Deep Learning
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Control of Mobile Robots
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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
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Machine Learning in Python
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Fundamentals of Project Planning and Management
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Leadership and Emotional Intelligence
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Entrepreneurship: Developing the Opportunity
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