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    Safety Testing:
                     u
                        ∑  Description: Specifically testing safety mechanisms (e.g., emergency stops, collision avoidance, force limits in
                        collaborative robots) to ensure they activate correctly and prevent harm.
                        ∑  Purpose: Crucial for any robot that interacts with humans or operates in sensitive environments.
                        Simulations:
                     u
                        ∑   Description: Running  the robot’s  software in a virtual environment (a simulator)  that  mimics the real  world.
                           This allows for testing complex scenarios, especially dangerous or time-consuming ones, without risking physical
                           damage to the robot or its environment.
                        ∑   Purpose: Rapid  prototyping,  testing  rare  events, training  Artificial  Intelligence  algorithms,  and performing
                           regression tests efficiently.

                     Iterative Process
                     Debugging and testing are not one-time events; they are continuous and iterative processes throughout the entire robot
                     development lifecycle. Finding a bug during testing leads to debugging, which leads to a fix, which then requires more
                     testing to ensure the fix worked and didn’t introduce new problems. This cycle repeats until the robot is stable, reliable,
                     and meets all its requirements.

                     In essence, debugging and testing are the final crucial hurdles that transform a collection of components and lines of code
                     into a functional, reliable, and safe robotic system. They are the quality control steps that ensure the robot, whether it’s
                     navigating the bylanes of Delhi or operating in an industrial plant, performs its tasks correctly and efficiently, ultimately
                     building trust in its autonomous capabilities.

                                                  AI Can Beat Humans in Games but Struggles with Common Sense
                                                AI systems like Deep Blue (chess), AlphaGo (Go), and OpenAI s systems
                                       (video games like Dota 2) have beaten the world’s best human players. These victories prove
                          BRAINY         how powerful AI can be in analysing millions of possible moves and strategies. However,
                           FACT        while AI can dominate in structured environments like games, it often fails in simple real-world
                                             reasoning. For example, an AI might “know” millions of game strategies but not
                                           understand that ice cream melts in the sun. That’s the current gap between artificial
                                                                     and human intelligence.

                                                              ROBOT DYNAMICS

                          Computing = the brain of robotics.
                          Boolean logic = based on true (1) and false (0).
                          Algorithms are step-by-step instructions to solve problems.
                          Flowcharts = graphical representation of algorithms.

                          Pseudocode = plain-language version of program logic.
                          Microcontrollers (Arduino) control sensors/actuators in real-time.
                          Single Board Computers (Raspberry Pi) handle complex tasks, multitasking, AI.
                          Controllers process input   logic   output.
                          Decision-making in robotics is rooted in Boolean operations.

                          Computing gives robots intelligence and autonomy.

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                                                                                                              Computing System

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