¶ Robotic Path Planning
¶ Robotics
When you watch a robot move with confidence—gliding through a warehouse, weaving around obstacles, mapping unknown corridors, or reaching gracefully toward an object—it’s easy to forget how much thinking happens beneath that motion. Every turn, every pause, every adjustment represents dozens of calculations, predictions, and decisions carried out in fractions of a second. Behind that seemingly simple movement lies one of the most essential areas of robotics: path planning. If robotics is the art of creating intelligent motion, then path planning is the quiet, meticulous craft that makes that motion purposeful, safe, and efficient. This course begins with that truth, because to understand robots deeply, you must understand how they decide where to go and how to get there.
Path planning touches every kind of robot. A warehouse robot navigating between shelving lanes. A self-driving car merging into traffic. A drone flying over rugged terrain. A robotic arm picking components from a tray. A search-and-rescue rover crossing unstable ground. Whether the movement spans centimeters or kilometers, indoors or outdoors, in two dimensions or three, the fundamental challenge is the same: given a goal, find a path that avoids trouble, obeys constraints, and adapts as the world changes. And the world always changes.
The roots of robotic path planning stretch back decades, shaped by mathematicians, computer scientists, and engineers searching for ways to describe motion in a world filled with obstacles. Early robots moved through environments that were heavily simplified—flat floors, predictable layouts, static objects—but real environments could never be reduced so easily. Walls have irregularities, floors have slopes, objects are misplaced, people move unpredictably, and sensors introduce uncertainty into every decision. Path planning grew from this tension between theoretical precision and real-world complexity. Over time, it evolved into a discipline that blends geometry, probability, optimization, control, and perception.
But the field is much more than a collection of algorithms. It is a mindset. It is a way of thinking about space, intention, and risk. A robot planning a path must balance competing needs. It must be safe, avoiding collisions at all costs. It must be efficient, especially when operating in large environments or under tight time constraints. It must be smooth, because jerky motions can damage payloads or reduce stability. It must be resilient, able to adjust when new obstacles appear, old paths become blocked, or conditions shift unexpectedly. Path planning is the art of navigating uncertainty without losing purpose.
Many people learning robotics for the first time underestimate how challenging this can be. They look at a map, draw a line from the starting point to the goal, and assume the robot can simply follow it. But robots do not see the world as humans do. They do not instinctively understand depth, context, or danger. They must interpret sensor data, predict outcomes, and choose actions based on models that are incomplete or noisy. The simplest-looking environment—just a room, a corridor, a set of shelves—contains complexities that require intelligent planning to navigate safely. This course aims to make those complexities feel intuitive, turning the opaque world of planning algorithms into a clear, navigable landscape.
One of the key lessons you will encounter throughout this course is that path planning is not a single problem but a family of problems. Global planning seeks long-range routes through large spaces, often using maps that summarize the environment. Local planning focuses on short-term movement, reacting to nearby obstacles and avoiding collisions on the fly. Some robots use hierarchical planners that blend long-range strategy with short-range agility. Others rely on sampling-based planners that explore possibilities randomly but intelligently, uncovering viable paths in high-dimensional spaces like those faced by robotic arms. And some planners rely on optimization, repeatedly refining a path until it balances cost, smoothness, and feasibility.
Then there are robots whose environments are dynamic. A self-driving car needs to predict the movement of other vehicles and pedestrians. A warehouse robot must adjust when another robot passes by. A drone navigating through a forest must adapt to wind gusts and moving branches. These cases require planners that incorporate time, uncertainty, and prediction into their reasoning. The robot cannot simply avoid obstacles; it must anticipate and react to them. This course treats these dynamic challenges not as corner cases but as central elements of modern robotics.
Path planning also intersects deeply with perception. A robot cannot plan effectively if it does not understand what surrounds it. Cameras, lidars, depth sensors, IMUs, ultrasonic sensors, and infrared systems all feed information into planning algorithms. But raw data is rarely enough. The robot must create internal representations—maps, cost grids, point clouds, occupancy grids—and process them in ways that help the planner make good decisions. The quality of a plan depends heavily on the quality of perception. Throughout this course, you will see how perception and planning form a loop, each strengthening the other.
Robotic arms bring their own challenges. Unlike mobile robots, which primarily navigate in two-dimensional spaces, manipulators operate in high-dimensional configuration spaces where each joint introduces another layer of complexity. Planning a path for a six-degree-of-freedom robotic arm can mean searching through a space that is already more complex than anything a mobile robot encounters. Add obstacles, narrow passages, or tasks requiring precision, and planning becomes even more demanding. Yet these challenges reveal some of the most beautiful concepts in robotics—how robots can maneuver with grace, accuracy, and robustness in tight, uncertain spaces.
People often assume that path planning is entirely algorithmic, but humans play an important role in shaping planners. Engineers design cost functions that reflect the priorities of a task. Safety-first planners penalize paths that come too close to obstacles. Energy-efficient planners reduce acceleration and jerk. High-speed planners focus on rapid motion while maintaining safety margins. Planners used in crowded environments may prioritize social comfort, giving humans more space or avoiding certain behaviors. In this course, you’ll learn how these priorities shape planning strategies and how tuning a planner can dramatically change a robot’s behavior.
Robots also need planners that work in real time. The world doesn’t pause to give them time to think. Even the most sophisticated planning algorithm is useless if it takes too long to compute a viable path. Balancing optimality and speed becomes an essential design decision. Some planners aim for the best possible path, while others aim for a path that is “good enough” but extremely fast to compute. Understanding how to make these trade-offs is one of the most important skills for building real robots, and it is something we will explore in depth throughout this course.
As robots become more autonomous, the need for advanced planning only grows. Self-driving cars rely on path planning to keep passengers safe while navigating chaotic roads. Delivery robots depend on planning to weave through foot traffic and avoid obstructions. Industrial robots use planning to coordinate with other machines on a factory floor. Even home robots, like those that vacuum or deliver items, need planners that allow them to function safely and efficiently in everyday environments.
The real magic of path planning emerges when robots operate collaboratively. Multiple robots working in the same space must plan in ways that avoid conflicts, optimize shared resources, and coordinate their goals. These multi-robot systems are becoming increasingly important in warehouses, agriculture, inspection, construction, and search-and-rescue missions. Planning for groups of robots introduces new layers of complexity—communication, shared maps, synchronized motion—but also new opportunities for efficiency and resilience. As you progress through this course, you’ll see how planning scales from individual robots to entire coordinated fleets.
One of the most inspiring trends in path planning is the growing influence of machine learning. While classical planning relies on carefully structured mathematical models, learning-based approaches allow robots to acquire planning behaviors from data, experience, or simulation. Robots can learn to navigate mazes, avoid obstacles, or optimize motion based on patterns rather than explicit formulas. Hybrid approaches blend the reliability of classical planning with the adaptability of learning. The goal is not to replace traditional planning but to enhance it, giving robots the flexibility to handle environments that are too complex to model precisely. This course will introduce these ideas in a grounded, practical way, showing how learning can complement planning rather than compete with it.
Path planning also has a philosophical side. It raises questions about how autonomous agents should behave, how they should balance risk and reward, how they should navigate shared spaces with humans, and how much autonomy is appropriate in life-critical scenarios. These questions matter as robots become more integrated into society. A robot’s path is not just a line on a map—it is a decision, an action, a presence in a world shared with people. Understanding the implications of those decisions is part of becoming a responsible roboticist.
Throughout this course, you will discover that the beauty of path planning lies in its blend of theory and practice. It is a field that rewards mathematical insight, but also rewards creativity, intuition, and experimentation. It teaches you to see space differently, think dynamically, and design motion that captures both intelligence and intention. By the time you progress through all one hundred articles, path planning will no longer feel abstract or intimidating. It will feel like a familiar way of thinking—a natural lens for understanding how robots move through the world.
Your exploration of Robotic Path Planning begins here.
¶ Robotic Path Planning
I. Foundations of Path Planning (20 Chapters)
1. Introduction to Robotic Path Planning
2. Configuration Space: Representing Robot Motion
3. Workspace vs. Configuration Space
4. Obstacle Representation in Configuration Space
5. Types of Path Planning Problems: Point-to-Point, Multi-Goal, Coverage
6. Path Planning Metrics: Optimality, Completeness, Efficiency
7. Basic Search Algorithms: Breadth-First Search, Depth-First Search
8. Dijkstra's Algorithm: Finding Shortest Paths
9. A* Search: Heuristics and Optimality
10. Grid-Based Path Planning
11. Introduction to Sampling-Based Planning
12. Random Sampling and Configuration Space Exploration
13. Probabilistic Roadmaps (PRMs): Building a Roadmap
14. Rapidly-exploring Random Trees (RRTs): Growing a Tree
15. Basic Path Planning for Mobile Robots
16. Path Planning for Manipulators: Joint Space vs. Task Space
17. Introduction to Kinematics and Inverse Kinematics
18. Forward and Inverse Kinematics in Path Planning
19. Basic Trajectory Generation: Linear and Polynomial Interpolation
20. Introduction to Robot Control and Motion Execution
II. Intermediate Path Planning Techniques (30 Chapters)
21. Advanced Graph Search Algorithms: Weighted A*, Jump Point Search
22. Hierarchical Path Planning: Multi-Resolution Grids, Quadtrees, Octrees
23. Multi-Goal Path Planning: Finding Optimal Paths to Multiple Destinations
24. Path Planning with Kinematic Constraints: Nonholonomic Systems
25. Planning for Car-like Robots: Reeds-Shepp and Dubins Curves
26. Sampling-Based Planning for High-Dimensional Configuration Spaces
27. Advanced PRM Techniques: Visibility-Based PRMs, Gaussian Sampling
28. Advanced RRT Techniques: RRT*, Informed RRT*, RRT#-Smart
29. Path Smoothing and Optimization: Splines, Gradient Descent
30. Trajectory Optimization: Time-Optimal, Energy-Optimal, Jerk-Limited
31. Introduction to Potential Fields for Path Planning
32. Artificial Potential Functions and their Limitations
33. Navigation Functions and Global Planning
34. Path Planning in Dynamic Environments: Time-Varying Obstacles
35. Velocity Obstacles and Collision Avoidance
36. Predictive Collision Avoidance
37. Multi-Robot Path Planning: Coordination and Conflict Resolution
38. Decentralized Multi-Robot Planning
39. Task Allocation and Path Planning for Multi-Agent Systems
40. Path Planning under Uncertainty: Probabilistic Planning
41. Belief Space Planning
42. Partially Observable Markov Decision Processes (POMDPs) for Robot Motion
43. Introduction to Machine Learning for Path Planning
44. Learning-Based Path Planning
45. Reinforcement Learning for Robot Navigation
46. Combining Sampling-Based Planning with Machine Learning
47. Path Planning in Cluttered Environments
48. Planning for Manipulation Tasks: Grasping and Object Manipulation
49. Constraint-Based Path Planning
50. Case Studies: Applications of Path Planning Algorithms
III. Advanced Path Planning and Specialized Topics (50 Chapters)
51. Advanced Trajectory Optimization Techniques: Direct and Indirect Methods
52. Optimal Control for Robot Motion Planning
53. Nonlinear Programming for Trajectory Optimization
54. Stochastic Path Planning: Markov Decision Processes (MDPs)
55. Robust Path Planning: Dealing with Uncertainty and Noise
56. Path Planning with Temporal Constraints: Time Windows and Sequencing
57. Planning for Human-Robot Collaboration: Shared Workspace Planning
58. Path Planning for Flexible Manipulators
59. Planning for Underactuated Robots
60. Non-Smooth Optimization for Path Planning
61. Geometric Path Planning: Cell Decomposition, Visibility Graphs
62. Path Planning in Continuous Configuration Spaces
63. Planning with Complex Kinematic Constraints
64. Path Planning for Aerial Robots and Drones
65. Path Planning for Underwater Robots
66. Path Planning for Space Robots
67. Path Planning for Medical Robots
68. Path Planning for Industrial Robots
69. Path Planning for Agricultural Robots
70. Path Planning for Social Robots
71. Path Planning for Humanoid Robots
72. Path Planning for Soft Robots
73. Path Planning for Micro/Nano Robots
74. Path Planning for Swarms of Robots
75. Path Planning in Virtual Environments
76. Path Planning for Augmented Reality Applications
77. Path Planning for Virtual Reality Applications
78. Real-time Path Planning: Fast Replanning and Adaptation
79. Hardware Acceleration for Path Planning Algorithms (GPUs, FPGAs)
80. Parallel Computing for Path Planning
81. Distributed Path Planning: Cloud Robotics
82. Path Planning Libraries and Software Tools (e.g., OMPL, MoveIt!)
83. Benchmarking and Evaluating Path Planning Algorithms
84. Performance Analysis and Tuning of Path Planning Systems
85. Debugging and Troubleshooting Path Planning Problems
86. Software Engineering for Path Planning
87. Version Control for Path Planning Projects
88. Collaborative Development of Path Planning Systems
89. Open Source Path Planning Projects and Contributions
90. Path Planning Education and Training
91. Path Planning Research and Development
92. Future Trends in Path Planning
93. Emerging Technologies in Path Planning
94. Ethical Considerations in Path Planning
95. Building a Complete Path Planning System
96. Integrating Path Planning with Robot Control
97. Deploying Path Planning Algorithms to Real-World Robots
98. Maintaining and Upgrading Path Planning Systems
99. Resources and Communities for Path Planning
100. Glossary of Path Planning Terms