#robot car

[cwhapd]

[voodoorhythmrecords]

robot taxi

[cybereverything]

Today I rode in an automated taxi.

It was pretty smooth.

[arduino-diy]

Unboxing and Assembling Crowbot Bolt Smart Robot Car

Hi everyone, today I am going to unbox and assemble the Crowbot Bolt an open source programmable Smart Robot Car. CrowBot BOLT is an open source smart programming car powered by ESP32 chip. It has a wealth of sensors (such as a light sensor, ultrasonic sensor, and line Tracking Sensor), comes with Wifi and Bluetooth functions, and can meet a variety of programming environments like Letscode, Micropython, and Arduino. It can quickly realize functions such as light tracking, line tracking, obstacle avoidance, remote control, and light show. As an entry-level STEAM educational robot, the cool and cute BOLT makes robot programming learning and teaching easy and fun.

Order Crowbot Bolt from here: https://www.elecrow.com/crowbot-bolt-programmable-smart-robot-car-steam-robot-kit.html?idd=3

[biggelectronics]

4-Wheel Arduino-Controlled Robot

Dabble-controlled 4-wheel robot — an easy-to-build and easy-to-control robot that can be controlled via Bluetooth using Dabble, an indigenously developed mobile application. An Arduino Uno board,4WD Four Wheel Drive Kit — A Smart Robot Car with Chassis and other accessories and tools are all you need to build your robot. To control it, download Dabble from Google Play and pair it with Bluetooth; then your robot is ready to go!

Components Required:

4WD Four Wheel Drive Kit — A Smart Robot Car with Chassis Arduino UNO Breadboard HC-05 Bluetooth Module Jumper wires Motor driver DC Terminal Block Motor Mounts

Connection:

To begin, we will construct the body of the 4 Wheel Robot.

Take the chassis and flip it over. Mount four motor mounts to this chassis with M3 bolts and nuts. M2 Nuts and Bolts are used to secure the DC Motors to the motor mounts. Attach wheels to the DC Motor’s shafts. As a result of flipping the assembly, your body is created. The microcontroller we’ll be using is the Arduino Uno. We will make all of the connections on it, but we will also use a breadboard to make the connections.

Make the connection shown in the diagram below.

Because the Arduino Uno does not have enough power to run four motors, we will use motor drivers. As a result of adding the motor driver, we will be able to provide the robot’s needed energy.

The left two motors are connected in parallel. Similarly, the right two motors are too connected in parallel. The connections are made as follows:

Enable Pins — Digital Pin 10 and 11

VCC — Arduino 5V

m1_dir1, m1_dir2, m2_dir1, m2_dir2 — Digital Pin 4, 5, 6, and 7

VC — External Battery

GND — GND of Arduino and Battery make sure we connect all the GND wires.

Then we need to add a module that will connect your robot to the Dabble App on your smartphone.

We’re using the Bluetooth HC05 Module. Connect it

Our robot will require an additional power source. In addition, we will use a DC Terminal Block or a DC Jack to connect the Arduino Uno to the battery.

Arduino Code

Upload the following code: code for arduino

Working

Using Dabble, we can easily connect our robot to our Smartphone.

You must connect the Bluetooth that you are working on before you can work with any of the modules.

There are numerous modules available in the App for additional functionality.

We’ll be working with the Gamepad Module today.

And we’ll be using the buttons on the left:

Move Forward — Move Up Down — Reverse Direction Left — Turn Left Right — Turn Right

Your DIY Dabble-controlled 4wheel robot is now ready for action!

[gyantechnolgy]

रोबोट (Robot) 🤖 बनने वाली कार 🚗

रोबोट बनाने वाली कार

robot car

[techyfrogs]

Indi Robot Car For Driving Lessons

Indi Robot Car For Driving Lessons

This Indi robot car from Sphero can teach your kids early driving lessons. In the modern era, computer programming has become the most in-demand skill. Pushing kids towards programming is a hard job, as it is not very glamourous and visual, which can attract kids. Spero has launched a new toy in the market for kids, which targets early learning of computer programming. Indi robot car | Credit:…

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[towardsrobotics-blog]

Automobile robotics: Applications and Methods - Towards Robotics

Automobile robotics: Applications and Methods

Introduction:

An automobile robot is a software-controlled machine that uses sensors and other technology to identify its environment and act accordingly. They work by combining artificial intelligence (AI) with physical robotic elements like wheels, tracks, and legs. Mobile robots are gaining increasing popularity across various business sectors. They assist with job processes and perform activities that are difficult or hazardous for human employees.

Structure and Methods:

The mechanical structure must be managed to accomplish tasks and attain its objectives. The control system consists of four distinct pillars: vision, memory, reasoning, and action. The perception system provides knowledge about the world, the robot itself, and the robot-environment relationship. After processing this information, sending the appropriate commands to the actuators that move the mechanical structure. Once the environment, and destination, or purpose of the robot is known, the robot’s cognitive architecture must plan the path that the robot must take to attain its goals.

The cognitive architecture reflects the purpose of the robot, its environment, and the way they communicate. Computer vision and identification of patterns are used to track objects. Mapping algorithms are used for the construction of environment maps. Motion planning and other artificial intelligence algorithms could eventually be used to determine how the robot should interact with each other. A planner, for example, might determine how to achieve a task without colliding with obstacles, falling over, etc. Artificial intelligence is called upon to play an important role in the treatment of all the information the robot collects to give the robot orders in the next few years. Nonlinear dynamics found in robots. Nonlinear control techniques utilize the knowledge and/or parameters of the system to reproduce its behavior. Complex algorithms benefit from nonlinear power, estimation, and observation.

Following are best-known control methods:

Computed torque control methods: A computed torque is defined using the second position derivatives, target positions, and mass matrix expressed in a conventional way with explicit gains for the proportional and derivative errors (feedback).

Robust control methods: These methods are similar to simulated methods of torque control, with the addition of a feedback variable depending on an arbitrarily small positive design constant E.

Sliding mode control methods: Increasing the controller frequency may be used to increase the system’s steady error. Taken to the extreme, the controller requires infinite actuator bandwidth if the design parameter E is set to zero, and the state error vanishes. This discontinuous controller is called a controller on sliding mode.

Adaptive methods: Awareness of the exact robot dynamics is relaxed compared to previous methods and this approach uses a linear assumption of parameters. These methods use feed-forward terminology estimation, thereby reducing the requirement for high gains and high frequency to compensate for uncertainties/disturbance in the dynamic model.

Invariant manifold method: the dynamic equation is broken down into components to perform functions independently.

Zero moment point control: This is a concept for humanoid robots associated, for example, with the control and dynamics of legged locomotion. It identifies the point around which no torque is generated by the dynamic reaction force between the foot and the ground, that is, the point at which the total horizontal inertia and gravity forces are equal to zero. This definition means the contact patch is planar and has adequate friction to prevent the feet from sliding

Navigation Methods: Navigation skills are the most important thing in the field of automobile robotics. The aim is for the robot to move in a known or unknown environment from one place to another, taking into account the sensor values to achieve the desired targets. This means that the robot must rely on certain factors such as perception (the robot must use its sensors to obtain valuable data), localization (the robot must use its sensors to obtain valuable data) The robot must be aware of its position and configuration, cognition (the robot must decide what to do to achieve its objectives), and motion control (the robot must calculate the input forces on the actuators to achieve the desired trajectory).

Path, trajectory, and motion planning:

The aim of path planning is to find the best route for the mobile robot to meet the target without collision, allowing a mobile robot to maneuver through obstacles from an initial configuration to a specific environment. It neglects the temporal evolution of motion. It does not consider velocities and accelerations. A more complete study is trajectory planning, with broader goals.

Trajectory planning involves finding the force inputs (control u (t)) to push the actuators so that the robot follows a q (t) trajectory which allows it to go from the initial to the final configuration while avoiding obstacles. To plan the trajectory it takes into account the dynamics and physical characteristics of the robot. In short, both the temporal evolution of the motion and the forces needed to achieve that motion are calculated. Most path and trajectory planning techniques are shared.

Applications of Automobile robotics:

A mobile robot’s core functions include the ability to move and explore, carry payloads or revenue-generating cargo, and complete complex tasks using an onboard system, such as robotic arms. While the industrial use of mobile robots is popular, particularly in warehouses and distribution centers, its functions may also be applied to medicine, surgery, personal assistance, and safety. Exploration and navigation at ocean and space are also among the most common uses of mobile robots.

Mobile robots are used to access areas, such as nuclear power plants, where factors such as high radiation make the area too dangerous for people to inspect themselves and monitor. Current automobile robotics, however, do not build robots that can withstand high radiation without having to compromise their electronic circuitry. Attempts are currently being made to invent mobile robots to deal specifically with those situations.

Other uses of mobile robots include:

shoreline exploration of mines

repairing ships

a robotic pack dog or exoskeleton to carry heavy loads for military troopers

painting and stripping machines or other structures

robotic arms to assist doctors in surgery

manufacturing automated prosthetics that imitate the body’s natural functions and

patrolling and monitoring applications, such as determining thermal and other environmental conditions

Cons and Pros of automobile robotics:

Their machine vision capabilities are a big benefit of automobile robots. The complex array of sensors that mobile robots use to detect their surroundings allows them to observe their environment accurately in real-time. That is especially valuable in constantly evolving and shifting industrial settings.

Another benefit lies in the onboard information system and AI used by AMRs. The autonomy provided by the ability of the mobile robots to learn their surroundings either through an uploaded blueprint or by driving around and developing a map allows for quick adaptation to new environments and helps in the continued pursuit of industrial productivity. Furthermore, mobile robots are quick and flexible to implement. These robots can make their own path for motion.

Some of the disadvantages are following

load-carrying limitation

More expensive and complex.

Communication challenges between robot and endpoint

Looking ahead in the future, manufacturers are trying to find more non-industrial applications for automobile robotics. Current technology is a mix of hardware, software, and advanced machine learning; it is considered solution-focused and rapidly evolving. AMRs are still struggling to move from one point to another; it is important to enhance spatial awareness. The design of the Simultaneous Localization and Mapping (SLAM) algorithm is one invention that is trying to solve this problem.

Hope you enjoyed this article. You may also want to check out my article on the concepts and basics of mobile robotics.