Students learn about the concepts of accuracy and approximation as they pertain to robotics, gain insight into experimental accuracy, and learn how and when to estimate values that they measure. Students also explore sources of error stemming from the robot setup and rounding numbers.

Students work as physicists to understand centripetal acceleration concepts. They also learn about a good robot design and the accelerometer sensor. They also learn about the relationship between centripetal acceleration and centripetal force governed by the radius between the motor and accelerometer and the amount of mass at the end of the robot's arm. Students graph and analyze data collected from an accelerometer, and learn to design robots with proper weight distribution across the robot for their robotic arms. Upon using a data logging program, they view their own data collected during the activity. By activity end , students understand how a change in radius or mass can affect the data obtained from the accelerometer through the plots generated from the data logging program. More specifically, students learn about the accuracy and precision of the accelerometer measurements from numerous trials.

Students learn that fats found in the foods we eat are not all the same; they discover that physical properties of materials are related to their chemical structures. Provided with several samples of commonly used fats with different chemical properties (olive oil, vegetable oil, shortening, animal fat and butter), student groups build and use simple LEGO MINDSTORMS(TM) NXT robots with temperature and light sensors to determine the melting points of the fat samples. Because of their different chemical structures, these fats exhibit different physical properties, such as melting point and color. This activity uses the fact that fats are opaque when solid and translucent when liquid to determine the melting point of each sample upon being heated. Students heat the samples, and use the robot to determine when samples are melted. They analyze plots of their collected data to compare melting points of the oil samples to look for trends. Discrepancies are correlated to differences in the chemical structure and composition of the fats.

Students analyze the relationship between wheel radius, linear velocity and angular velocity by using LEGO(TM) MINDSTORMS(TM) NXT robots. Given various robots with different wheel sizes and fixed motor speeds, they predict which has the fastest linear velocity. Then student teams collect and graph data to analyze the relationships between wheel size and linear velocity and find the angular velocity of the robot given its motor speed. Students explore other ways to increase linear velocity by changing motor speeds, and discuss and evaluate the optimal wheel size and desired linear velocities on vehicles.

Students create four-legged walking robots and measure how far they travel across different types of surfaces. They design and create "shoes" to add to the robots' feet and observe the effect of their modifications on the net distance traveled across the various surface types. This activity illustrates how the specialized locomotive features of different species help them to survive or thrive in their habitat environments. The activity is best as an enrichment tool that follows a lesson that introduces the concept of biological adaptation to students.

This lesson explores the similarities between how a human being moves/walks and how a robot moves. This allows students to see the human body as a system, i.e., from the perspective of an engineer. It shows how movement results from (i) decision making, i.e., deciding to walk and move, and (ii) implementing the decision by conveying the decision to the muscle (human) or motor (robot).

Students learn more about assistive devices, specifically biomedical engineering applied to computer engineering concepts, with an engineering challenge to create an automatic floor cleaner computer program. Following the steps of the design process, they design computer programs and test them by programming a simulated robot vacuum cleaner (a LEGO® robot) to move in designated patterns. Successful programs meet all the design requirements.

6.270 is a hands-on, learn-by-doing class, in which participants design and build a robot that will play in a competition at the end of January. The goal for the students is to design a machine that will be able to navigate its way around the playing surface, recognize other opponents, and manipulate game objects. Unlike the machines in Introduction to Design (2.70), 6.270 robots are totally autonomous, so once a round begins, there is no human intervention. The goal of 6.270 is to teach students about robotic design by giving them the hardware, software, and information they need to design, build, and debug their own robot.

Students visualize and interact with concepts already learned, specifically algebraic equations and solving for unknown variables. They construct a balancing seesaw system (LEGO® Balance Scale) made from LEGO MINDSTORMS® parts and digital components to mimic a balancing scale. They are given example algebraic equation problems to analyze, configure onto the balance scale, and evaluate by manipulating LEGO pieces and gram masses that represent terms of an equation such as unknown variables, coefficients and integers. Digital light sensors, built into the LEGO Balance Scale, detect any balance or imbalances displayed on the balancing scale. The LEGO Balance Scale interactively issues a digital indication of balance or imbalance within the system. If unbalanced, students continue using the LEGO Balance Scale until they are confident in their understanding of solving algebraic equations. The goal is for students to become confident in solving algebraic equations by fundamentally understanding the basics of algebra and real-world algebraic applications.

Here's a super fun science activity that really gets kids thinking! Children will explore  and center of gravity by creating a balancing robot!

Students use ultrasonic sensors and LEGO© MINDSTORMS© NXT robots to emulate how bats use echolocation to detect obstacles. They measure the robot's reaction times as it senses objects at two distances and with different sensor threshold values, and again after making adjustments to optimize its effectiveness. Like engineers, they gather and graph data to analyze a given design (from the tutorial) and make modifications to the sensor placement and/or threshold values in order to improve the robot's performance (iterative design). Students see how problem solving with biomimicry design is directly related to understanding and making observations of nature.

Students learn about the similarities between the human brain and its engineering counterpart, the computer. Since students work with computers routinely, this comparison strengthens their understanding of both how the brain works and how it parallels that of a computer. Students are also introduced to the "stimulus-sensor-coordinator-effector-response" framework for understanding human and robot actions.

Students identify different bridge designs and construction materials used in modern day engineering. They work in construction teams to create paper bridges and spaghetti bridges based on existing bridge designs. Students progressively realize the importance of the structural elements in each bridge. They also measure vertical displacements under the center of the spaghetti bridge span when a load is applied. Vertical deflection is measured using a LEGO MINDSTORMS(TM) NXT intelligent brick and ultrasonic sensor. As they work, students experience tension and compression forces acting on structural elements of the two bridge prototypes. In conclusion, students discuss the material properties of paper and spaghetti and compare bridge designs with performance outcomes.

At its core, the LEGO MINDSTORMS(TM) NXT product provides a programmable microprocessor. Students use the NXT processor to simulate an experiment involving thousands of uniformly random points placed within a unit square. Using the underlying geometry of the experimental model, as well as the geometric definition of the constant π (pi), students form an empirical ratio of areas to estimate a numerical value of π. Although typically used for numerical integration of irregular shapes, in this activity, students use a Monte Carlo simulation to estimate a common but rather complex analytical form the numerical value of the most famous irrational number, π.

Students learn about gear ratios and power by operating toy mechanical cranes of differing gear ratios. They attempt to pick up objects with various masses to witness how much power must be applied to the system to oppose the force of gravity. They learn about the concept of gear ratio and practice calculating gear ratios on worksheets, discovering that smaller gear ratios are best for picking objects up quickly, and larger gear ratios make it easier to lift heavy objects.

Posed with a paradigmatic engineering problem, students consider and explore mathematical algorithms and/or geometric concepts to devise possible solutions. The problem: How should a robotic vacuum move in order to best clean a floor of unknown shape and dimensions? They grapple with what could be a complex problem by brainstorming ideas, presenting the best idea for a solution and analyzing all presented solutions, and then are introduced to an elegant solution. Rather than elaborately calculating the most efficient route and keeping track of which tiles the robot has visited, a random number generator determines which direction the robot will take when it hits a barrier. Students are able to visually confirm how an unfamiliar programming concept (a random number generator) can make for a simple and efficient program that causes an NXT robot (that is suitably equipped) to clean a bare floor. Then students think of other uses for random numbers.

Students continue their exploration of the human senses and their engineering counterparts, focusing on the auditory sense. Working in small groups, students design, create and run programs to control the motion of LEGO® TaskBots. By doing this, they increase their understanding of the use and function of sound sensors, gain experience writing robot programs, and reinforce their understanding of the sensory process.

Students use the robot paths they documented during the associated Robots on Ice Engineering Challenge activity to learn about and then make artwork. During the previous activity, students recorded the path of their robots through a maze in order to collect data during a remote research simulation. Now, they take a new look at the robot paths, seeing them from an art perspective as continuous line drawings. Students learn about Picasso’s famous works of art that used the same technique. Then they learn the artistic definition of a line and see examples of how it is used in different art pieces; they practice making continuous line drawings and then create sculptures of their drawings using colorful wire. A PowerPoint® presentation is provided to guide the activity.

Students gain a deeper understanding of how sound sensors work through a hands-on design challenge involving LEGO MINDSTORMS(TM) NXT taskbots and sound sensors. Student groups each program a robot computer to use to the sound of hand claps to control the robot's movement. They learn programming skills and logic design in parallel. They experience how robots can take sensor input and use it to make decisions to move and turn, similar to the human sense of hearing. A PowerPoint® presentation and pre/post quizzes are provided.

Students learn about and practice converting between fractions, decimals and percentages. Using a LEGO® MINDSTORMS® NXT robot and a touch sensor, each group inputs a fraction of its choosing. Team members convert this same fraction into a decimal, and then a percentage via hand calculations, and double check their work using the NXT robot. Then they observe the robot moving forward and record that distance. Students learn that the distance moved is a fraction of the full distance, based on the fraction that they input, so if they input ½, the robot moves half of the original distance. From this, students work backwards to compute the full distance. Groups then compete in a game in which they are challenged to move the robot as close as possible to a target distance by inputting a fraction into the NXT bot.