Robot Planning & Control: How Robots Decide, Plan, and Act in the Physical World

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Robot Planning & Control: How Robots Decide, Plan, and Act in the Physical World

Author: Landon Pierce

Description: A warehouse robot glides through an aisle, smoothly avoiding a misplaced pallet. A drone recovers mid-flight after a gust of wind shoves it sideways. These moments of effortless autonomy are powered by a sophisticated pipeline of algorithms—one that most software engineers have never been formally taught to design. Robot Planning & Control: How Robots Decide, Plan, and Act in the Physical World bridges that gap, translating the abstract concepts of robotics into concrete, intuitive mental models for engineers who think in code. Written by Landon Pierce, this book follows a 'concepts before equations' philosophy, making the architecture of autonomous systems accessible without drowning you in derivations. It mirrors the actual software-hardware pipeline: from high-level decision making, through the physics of motion, into path and motion planning, then down to real-time control, and finally out to integrated systems like warehouse fleets, self-driving cars, drones, and humanoid robots. Each chapter ends with a real-world case study—whether it's how Amazon Robotics avoids deadlocks or how Waymo handles intersections—so you see exactly how theory becomes product. • Understand the full autonomous loop: perception to action, reactive vs. deliberative planning, and levels of autonomy. • Master the physical vocabulary of robotics: degrees of freedom, kinematics, dynamics, and balance—explained through software analogies. • Dive into the algorithms that matter: A*, Dijkstra, PID control, and multi-step planning—all focused on practical trade-offs, not proofs. This is not a theoretical textbook. It is a systems-engineering guide for professionals who want to design, evaluate, or integrate robotic planning and control systems. You will learn why a clean map can still fail due to friction, how to tune a PID controller by thinking of it as a thermostat with memory, and what it takes to coordinate a fleet of a hundred robots without collisions. If you are a software engineer moving into robotics, a technical manager evaluating autonomous platforms, or an engineering student hungry for a system-level view, Robot Planning & Control gives you the conceptual framework to think physically, plan intelligently, and act with confidence.

AI summary: This book provides an intuitive, systems-level understanding of robot planning and control for software engineers. It covers path planning, PID control, kinematics, dynamics, and multi-robot coordination through real-world case studies like warehouse robots and autonomous vehicles, emphasizing concepts before mathematics.

Ideal para

Software engineers transitioning into robotics, technical product managers, and engineering students.

Perfil do leitor

A software engineer with 3+ years of experience looking to understand the full stack of autonomous systems and apply their coding skills to robotics.

Intenção de busca

Engineers seeking a practical, concept-first introduction to robot planning and control algorithms and their real-world applications.

Tipo de conteúdo

technical engineering guide

Key topics: Robot Planning, Control Systems, Path Planning, Kinematics, Dynamics, Autonomous Systems, PID Control, Multi-Robot Coordination, Warehouse Robots, Self-Driving Cars

Sumário

1. Introduction: The Engineer's Guide to the Physical World (introduction)
2. From Perception to Action (part)
3. Why Planning Matters (chapter)
4. The Action Problem (section)
5. Goals and Constraints (section)
6. Decisions Under Uncertainty (section)
7. The Planning Pipeline (section)
8. Modern Autonomous Systems (section)
9. Robot Decision Making (chapter)
10. Reactive Behavior (section)
11. Rule-Based Systems (section)
12. Planning Systems (section)
13. Autonomous Decision Loops (section)
14. Real-World Examples (section)
15. Levels of Autonomy (chapter)
16. Human-Controlled Systems (section)
17. Assisted Systems (section)
18. Semi-Autonomous Systems (section)
19. Fully Autonomous Systems (section)
20. Future Trends (section)
21. Robot Motion (part)
22. Understanding Robot Motion (chapter)
23. Degrees of Freedom (section)
24. Robot Joints (section)
25. Robot Movement (section)
26. Constraints (section)
27. Motion Complexity (section)
28. Kinematics (chapter)
29. Forward Kinematics (section)
30. Inverse Kinematics (section)
31. Robot Arms (section)
32. Mobile Robots (section)
33. Practical Applications (section)
34. Dynamics (chapter)
35. Forces and Motion (section)
36. Momentum (section)
37. Stability (section)
38. Energy (section)
39. Robot Dynamics (section)
40. Balancing and Stability (chapter)
41. Static Stability (section)
42. Dynamic Stability (section)
43. Walking Robots (section)
44. Humanoid Balance (section)
45. Real-World Challenges (section)
46. Planning (part)
47. Path Planning Fundamentals (chapter)
48. What Is Path Planning? (section)
49. Free Space (section)
50. Obstacles (section)
51. Cost Functions (section)
52. Practical Planning Problems (section)
53. Search Algorithms (chapter)
54. Graph Search (section)
55. Dijkstra (section)
56. A* (section)
57. Heuristics (section)
58. Real Applications (section)
59. Motion Planning (chapter)
60. Motion Constraints (section)
61. Trajectory Planning (section)
62. Dynamic Environments (section)
63. Collision Avoidance (section)
64. Planning in Practice (section)
65. Multi-Step Planning (chapter)
66. Task Planning (section)
67. Goal Decomposition (section)
68. Sequencing Actions (section)
69. Adaptive Planning (section)
70. Intelligent Behaviors (section)
71. Control Systems (part)
72. What Is Control? (chapter)
73. Open-Loop Control (section)
74. Closed-Loop Control (section)
75. Feedback (section)
76. Stability (section)
77. Modern Controllers (section)
78. PID Control (chapter)
79. Proportional Control (section)
80. Integral Control (section)

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