Introduction to obstacle avoidance

Obstacle avoidance is a critical skill that enables robots to navigate their environment without colliding with objects. Robots use various sensors and intelligent algorithms to detect obstacles and make decisions on navigating around them safely in FreeSpace. The course "FreeSpace" on www.coderobo.ai is a great resource to introduce obstacle avoidance to kids.

It's amazing how babies learn to avoid obstacles and crawl in free space so early in their lives. But robots have to be programmed to avoid obstacles and move freely to reach their goals. Just like human eyes and ears, robots rely on different types of sensors to gather information about their surroundings. Robots use this information to make decisions and control the motors connected to the wheels to drive the robot toward its goal.

Obstacle avoidance has been a fundamental challenge in robotics, and over the years, scientists and engineers have made significant progress in developing effective solutions. In the early days of robotics, obstacle avoidance was primarily based on simple techniques such as mechanical bumpers or limit switches. These mechanisms allowed robots to physically detect and react to collisions with objects. As sensor technologies evolved, robots gained the ability to detect obstacles in their surroundings more accurately using ultrasonic sensors, cameras, and lidar.

Similarly, algorithms play a critical role in path planning and obstacle avoidance in robotics. Dijkstra's algorithm is widely used for finding the shortest path between two points in a graph. It can be adapted for path planning by representing obstacles as impassable regions in the graph. Incremental improvements in algorithms such as the A* algorithm, Rapidly-exploring Random Trees (RRT), and Potential Field Algorithms have improved the state-of-the-art of these algorithms used in the robots we see today in our homes, airports, shopping malls, etc.

In other words, any robotic system can be broken down into three key operations: Sense - Plan - Act.

Sense refers to the ability of robots to gather information about their surroundings using various sensors. In the FreeSpace course, students are introduced to blocks such as "Is at Goal" and "Distance to front/back/left/right" to sense their environment. With these blocks, robots can sense obstacles, measure the distance from walls, or determine if they have reached their goal. These sensors are simple to use but also very powerful.

Plan represents the intellectual capabilities of robots to process the information gathered from their sensors and make intelligent decisions. In the FreeSpace course, students write simple algorithms to plan their paths around obstacles and reach their goals. Students can apply the knowledge of conditional statements and loops learned in the Introduction to Programming course.

Act involves the execution of actions based on the decisions made during the planning phase. Robots take actions such as moving an arm to pick up an object, saying hello, or turning on a light on the robot. In CodeRobo.AI, robots move around the room to reach their goals. It uses blocks such as "move forward/backward" and "turn left/right." Students also use more advanced blocks, such as "set linear and angular velocity." This introduces students to the fundamental principles of controlling mobile robots.

By understanding the Sense-Plan-Act framework and exploring the exciting possibilities of obstacle avoidance, young learners can develop the skills and knowledge needed to navigate the fascinating world of robotics. The FreeSpace course on www.coderobo.ai provides a fun and educational platform to learn about these concepts and unleash their creativity in the field of robotics. So, get ready to sense, plan, and act your way into the exciting realm of robots!

The CodeRobo.AI team's motto is to nurture kids' curiosity in robotics and coding. We look forward to you joining our mission of making coding and robots accessible to more kids. We would love to hear about your experience using CodeRobo.AI to teach students at your institute. The CodeRobo.AI team is working on many new features to enhance the teaching experience, which helps students better understand the concepts. Please join our newsletter for more updates. 

Keywords: Obstacle avoidance, Robotics, Sense Plan Act, Robots, Sensors, Algorithms, Path planning, FreeSpace, CodeRobo.AI, Machine learning, Lidar, Radar, Artificial intelligence, Robot programming, Intelligent decision-making, Autonomous navigation, Robot sensors, Robotics education, Robot control, Robotics for kids