{"product_id":"robotics-algorithms-for-motion-perception-and-cont","title":"Robotics Algorithms For Motion Perception And Control In Autonomous Systems A Hands On Guide For Engineers And Researchers Covering Path Planning Sensor Fusion Real Time Control And Practical Implementation","description":"\u003cdiv\u003eRobotics Algorithms for Motion Planning and Control\u003cbr\u003e\n✔️ Single copy with full source code samples and GitHub resources \u003cp\u003eRobotics Algorithms is a comprehensive reference for engineers, researchers, and advanced students focusing on motion perception, planning, sensor fusion, and real-time control in autonomous systems. It’s a practical guide for labs, universities, and industry teams building autonomous drones, surgical bots, industrial manipulators, or multi-agent platforms.\u003c\/p\u003e \u003cp\u003e✅ Precise motion planning with A*, RRT, PRM and real-time optimization \u003cbr\u003e\n✅ Sensor fusion \u0026amp; SLAM combining vision, lidar, and inertial data with Kalman filters and particle methods \u003cbr\u003e\n✅ Advanced control systems including PID, feedback linearization, deep reinforcement learning, and model predictive control \u003cbr\u003e\n✅ Swarm \u0026amp; multi-robot coordination with decentralized planning and market-based task allocation \u003cbr\u003e\n✅ Learning-enabled robotics with supervised, unsupervised, and evolutionary methods \u003cbr\u003e\n✅ Human-robot interaction with gesture recognition, force feedback, and shared autonomy \u003cbr\u003e\n✅ Full-code integration using Python, C++, and ROS across Gazebo, MuJoCo, and Webots \u003cbr\u003e\n✅ Application coverage for autonomous vehicles, medical robots, UAVs, prosthetics, deep-sea rovers, and space exploration \u003cbr\u003e\n✅ Update: GitHub repository with source code samples, infographics, and more \u003cbr\u003e\u003c\/p\u003e \u003cp\u003e💡 What is a good reference for motion planning and control algorithms in autonomous robotics?\nThis handbook provides clear definitions, practical notes, and code-based guidance.\n- Clear explanations of A*, RRT, PRM, MPC and real-time optimization\n- Sensor fusion, SLAM, Kalman filters, particle methods for localization\n- Real-world code examples in Python, C++, and ROS across Gazebo, MuJoCo, Webots\n- Applications across autonomous vehicles, medical robots, UAVs, prosthetics, and space tech\n\u003c\/p\u003e✝️\u003c\/div\u003e","brand":"Amazon","offers":[{"title":"Default Title","offer_id":48885019148523,"sku":"VXB0F5X4NMWT","price":90.95,"currency_code":"USD","in_stock":true}],"url":"https:\/\/vxb.com\/products\/robotics-algorithms-for-motion-perception-and-cont","provider":"VXB Bearings","version":"1.0","type":"link"}