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Robotics Algorithms

Optimizing Motion Perception, and Control in Autonomous Systems

🧠 Master the Algorithms Powering Tomorrow’s Robots

Robotics Algorithms is your comprehensive guide to the core algorithms that enable modern robots to move, perceive, and think—designed as a glossary-style reference for engineers, researchers, and advanced students who want depth without the fluff.

Whether you're building autonomous drones, surgical bots, industrial manipulators, or multi-agent systems—this book delivers the tools you need to optimize performance at every level of robotic intelligence.

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About the Book

🧠 Master the Algorithms Powering Tomorrow’s Robots

Robotics Algorithms is your comprehensive guide to the core algorithms that enable modern robots to move, perceive, and think—designed as a glossary-style reference for engineers, researchers, and advanced students who want depth without the fluff.

Whether you're building autonomous drones, surgical bots, industrial manipulators, or multi-agent systems—this book delivers the tools you need to optimize performance at every level of robotic intelligence.

🚀 What You’ll Learn

âś… Precise Motion Planning: Explore A*, RRT, PRM, and real-time optimization techniques to enable collision-free, energy-efficient trajectories—even in complex environments.

âś… Sensor Fusion & SLAM: Fuse vision, lidar, and inertial data with Kalman filters and particle methods for reliable localization and mapping in real-world conditions.

âś… Advanced Control Systems: From PID and feedback linearization to deep reinforcement learning and model predictive control—learn to tame high-dimensional dynamics.

âś… Swarm & Multi-Robot Systems: Implement scalable coordination, decentralized planning, and market-based task allocation in cooperative fleets.

âś… Learning-Enabled Robotics: Integrate supervised, unsupervised, and evolutionary algorithms to create robots that adapt, optimize, and evolve.

âś… Human-Robot Interaction: Develop intuitive, responsive systems with gesture recognition, force feedback, and shared autonomy.

âś… Full-Code Integration: Apply your knowledge using Python, C++, and ROS across simulated and physical platforms including Gazebo, Mujoco, and Webots.

âś… Application-Driven Coverage: Build solutions for autonomous vehicles, medical robots, UAVs, prosthetics, deep-sea rovers, and space exploration.

đź”§ Why This Book?

  • 📚 Glossary-Driven Format: Skip the filler. Each algorithm is defined, contextualized, and referenced with practical relevance.
  • đź§© Modular Chapters: Jump directly to the domain you’re working on—motion planning, SLAM, control, or multi-robot coordination.
  • ⚙️ Engineer's Reference: Ideal for field engineers, roboticists, and AI developers who need fast access to proven methods and implementation notes.

👨💻 For Engineers, Researchers, and Builders of the Future

Whether you're prototyping cutting-edge robots or enhancing existing systems, this book helps you go from concept to implementation with precision and clarity.

Grab your copy of Robotics Algorithms now and start building systems that move, think, and act with purpose.

UPDATE: This book now has a github repository with all source code samples, infographics and more.

From the Editor at Burst Books — Gareth Thomas

A Smarter Kind of Learning Has Arrived — Thinking on Its Own.

Forget tired textbooks from years past. These AI-crafted STEM editions advance at the speed of discovery. Each page is built by intelligence trained on thousands of trusted sources, delivering crystal-clear explanations, flawless equations, and functional examples — all refreshed through the latest breakthroughs.

Best of all, these editions cost a fraction of traditional texts yet surpass expectations. You’re gaining more than a book — you’re enhancing the mind’s performance.

Explore BurstBooksPublishing on GitHub to find technical samples, infographics, and additional study material — a complete hub that supports deeper, hands-on learning.

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About the Author

gareth thomas

Gareth Morgan Thomas is a qualified expert with extensive expertise across multiple STEM fields. Holding six university diplomas in electronics, software development, web development, and project management, along with qualifications in computer networking, CAD, diesel engineering, well drilling, and welding, he has built a robust foundation of technical knowledge.

Educated in Auckland, New Zealand, Gareth Morgan Thomas also spent three years serving in the New Zealand Army, where he honed his discipline and problem-solving skills. With years of technical training, Gareth Morgan Thomas is now dedicated to sharing his deep understanding of science, technology, engineering, and mathematics through a series of specialized books aimed at both beginners and advanced learners.

Contents

Table of Contents

Chapter 1. Foundations of Robotics Algorithms

Section 1. Mathematical Foundations

  • Linear Algebra in Robotics
  • Probability and Bayesian Inference
  • Optimization Techniques for Robotics

Section 2. Kinematics and Dynamics

  • Forward and Inverse Kinematics
  • Jacobians and Singularities
  • Newton-Euler and Lagrange Methods

Section 3. Motion Planning Fundamentals

  • Configuration Space (C-Space)
  • Degrees of Freedom in Robotics
  • Holonomic vs. Non-Holonomic Constraints

Chapter 2. Motion Planning Algorithms

Section 1. Classical Path Planning

  • A* Algorithm
  • Dijkstra’s Algorithm
  • Rapidly-exploring Random Trees (RRT)

Section 2. Probabilistic Roadmaps

  • PRM (Probabilistic Roadmaps)
  • Lazy PRM and PRM*
  • Sampling-Based Motion Planning

Section 3. Optimization-Based Path Planning

  • Trajectory Optimization (CHOMP)
  • Covariant Hamiltonian Optimization
  • Stochastic Optimization in Planning

Section 4. Real-Time Motion Planning

  • Dynamic Window Approach (DWA)
  • Elastic Bands and Elastic Strips
  • Model Predictive Control (MPC)

Chapter 3. Robot Perception and Sensor Fusion

Section 1. Computer Vision for Robotics

  • Feature Detection (SIFT, SURF, ORB)
  • Optical Flow Algorithms
  • SLAM-based Visual Odometry

Section 2. Lidar and Depth Sensing

  • Point Cloud Processing Algorithms
  • Iterative Closest Point (ICP)
  • Gaussian Mixture Models for Segmentation

Section 3. Sensor Fusion Techniques

  • Kalman Filters (EKF, UKF)
  • Particle Filters for Localization
  • Multi-Sensor Data Fusion

Chapter 4. Simultaneous Localization and Mapping (SLAM)

Section 1. Probabilistic SLAM

  • Rao-Blackwellized Particle Filters
  • Factor Graph SLAM
  • Bundle Adjustment for SLAM

Section 2. Graph-Based SLAM

  • Pose Graph Optimization
  • Loop Closure Detection Algorithms
  • iSAM (Incremental Smoothing and Mapping)

Section 3. Visual-Inertial SLAM

  • ORB-SLAM
  • VINS-Mono (Visual-Inertial Navigation)
  • MSCKF (Multi-State Constraint Kalman Filter)

Chapter 5. Control Algorithms for Robotics

Section 1. Classical Control Techniques

  • Proportional-Integral-Derivative (PID) Control
  • State-Space Controllers
  • Feedback Linearization

Section 2. Model Predictive Control (MPC)

  • Linear and Nonlinear MPC
  • Robustness and Constraints in MPC
  • Receding Horizon Control

Section 3. Reinforcement Learning in Control

  • Policy Gradient Methods
  • Deep Q-Networks (DQN)
  • Model-Free vs. Model-Based RL

Chapter 6. Multi-Robot Systems and Swarm Intelligence

Section 1. Cooperative Multi-Robot Systems

  • Task Allocation Algorithms (Auction-based, Market-based)
  • Consensus Algorithms
  • Distributed SLAM for Multi-Robot Teams

Section 2. Swarm Robotics

  • Particle Swarm Optimization (PSO)
  • Ant Colony Optimization (ACO)
  • Artificial Potential Fields for Swarm Navigation

Section 3. Communication and Coordination

  • Leader-Follower Control
  • Decentralized Path Planning
  • Game-Theoretic Approaches

Chapter 7. Learning and Adaptation in Robotics

Section 1. Machine Learning for Robotics

  • Supervised and Unsupervised Learning in Robotics
  • Dimensionality Reduction Techniques
  • Imitation Learning and Behavioral Cloning

Section 2. Reinforcement Learning for Robotics

  • Proximal Policy Optimization (PPO)
  • Soft Actor-Critic (SAC)
  • Hierarchical Reinforcement Learning

Section 3. Evolutionary Algorithms in Robotics

  • Genetic Algorithms for Optimization
  • Neuroevolution in Robotics
  • Policy Search in Evolutionary Robotics

Chapter 8. Human-Robot Interaction and Haptics

Section 1. Gesture and Speech Recognition

  • Dynamic Time Warping (DTW) for Gesture Recognition
  • Hidden Markov Models (HMM) for Speech Processing
  • Neural Networks for Multimodal Interaction

Section 2. Haptic Feedback and Teleoperation

  • Force Feedback Algorithms
  • Admittance and Impedance Control
  • Tactile Sensor Integration

Section 3. Social and Collaborative Robotics

  • Predictive Modeling for Human-Robot Interaction
  • Learning from Demonstration (LfD)
  • Shared Autonomy Systems

Chapter 9. Robotic Manipulation and Grasping

Section 1. Grasp Planning Algorithms

  • Dexterous Hand Planning
  • Grasp Quality Metrics
  • Contact-based Grasp Optimization

Section 2. Manipulation in Cluttered Environments

  • Motion Primitives for Manipulation
  • Physics-based Simulations for Manipulation
  • Tactile-based Object Recognition

Section 3. Industrial and Assistive Robotics

  • Assembly Line Automation
  • Robotic Prosthetics and Assistive Devices
  • AI-driven Dexterous Manipulation

Chapter 10. Legged and Aerial Robotics

Section 1. Bipedal and Quadrupedal Locomotion

  • Zero Moment Point (ZMP) for Walking Stability
  • SLIP Model for Running Robots
  • Reinforcement Learning for Locomotion

Section 2. Aerial Robotics and UAVs

  • Multi-Rotor Dynamics and Control
  • Path Planning for UAV Swarms
  • Vision-Based UAV Navigation

Section 3. Bio-Inspired Robotics

  • Soft Robotics and Morphological Computation
  • Fuzzy Logic Control for Adaptive Behaviors
  • Biohybrid Systems and Neuromorphic Computing

Chapter 11. Programming Implementations in Robotics

Section 1. ROS (Robot Operating System) Implementations

  • Navigation Stack Overview
  • SLAM and Perception with ROS
  • Motion Planning with MoveIt!

Section 2. Python and C++ Implementations

  • OpenCV for Computer Vision in Robotics
  • Pytorch and TensorFlow for Reinforcement Learning
  • Real-Time Control Algorithms in C++

Section 3. Simulation and Testing Environments

  • Gazebo for Physics-Based Simulations
  • Mujoco for Reinforcement Learning Experiments
  • Webots for Multi-Robot Simulations

Chapter 12. Real-World Applications of Robotics Algorithms

Section 1. Autonomous Vehicles

  • Perception and Decision-Making Pipelines
  • Behavior Cloning for Self-Driving Cars
  • Motion Planning for Autonomous Navigation

Section 2. Medical Robotics

  • Robotic Surgery Algorithms
  • Computer-Assisted Diagnosis and Imaging
  • AI-Assisted Prosthetic Control

Section 3. Space and Underwater Robotics

  • SLAM for Extraterrestrial Navigation
  • AUVs (Autonomous Underwater Vehicles) for Deep-Sea Exploration
  • Terrain Mapping and Exploration Algorithms

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