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Understanding the technology used to fuel Robot Soccer

Robot soccer is one of the oldest technology advancements since the 90s. It sprang up as early as 1992 and has been making waves ever since.

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Robot soccer is one of the oldest technology advancements since the 90s. It sprang up as early as 1992 and has been making waves ever since. Soccer itself is a technical sport that makes use of many cognitive functions. It’s a full-body sport that requires intelligent beings.

Translating these skills into robots is, therefore, not an easy task. Interestingly, scientists are some of the most resilient people who take impossible situations as a challenge. This has brought about a lot of advancements in Robot soccer. You may wonder what technology is used to fuel the gameplay of Robot soccer. At FuturePlay News we bring you the in-depth look into robot soccer technology and how it has shaped this exciting sport.

Robotics Hardware

The robots used in robot soccer have many different motors, sensors, microcontrollers, and power sources. These parts allow the robots to move, detect their surroundings, and perform tasks like kicking a ball. Although there are different technologies proposed to make a robot more lifelike, the following are underlying technologies used to make robot soccer a success.

Perception Systems

Robot soccer uses a perception system technology. Robots can see the field thanks to cameras and other sensors that let them locate the ball, keep tabs on their opponents, and recognize their teammates. Information is gleaned from these visual inputs with the use of image processing algorithms and computer vision techniques.

Motion Control

The robots’ motion is managed by motion control systems. They make certain the robots can move across the pitch, change directions, dribble the ball, and complete other soccer-related tasks. To achieve the intended motions, the relevant motor commands are generated using motion control algorithms, which frequently employ feedback control approaches.

Decision-Making Algorithms

Robots need the ability to reason through the game situation, plan their moves strategically, and select the best possible options as they compete. Robots are able to make strategic decisions based on their observations of the game thanks to a variety of algorithms, including rule-based systems, behaviour-based systems, and even more advanced approaches, such as machine learning and AI techniques.

Communication and Coordination

Team-based robot soccer requires excellent inter-robot communication and coordination. Effective strategy execution requires robots to communicate data, coordinate their movements, and work together. Robots are able to work together, pass the ball, and adjust to the activities of their teammates and opponents thanks to communication protocols and algorithms. This is an important component that we will take a deeper dive into.

Simulation and Modeling

Robot soccer relies heavily on computer simulation. It paves the way for algorithm and strategy development in a simulated setting before being applied to actual robots. Simulation software models the physical world accurately, and models of the robot and the playing field are also included.

Human-Computer Interfaces

During the prototyping, testing, and demonstration phases, human-computer interfaces are employed to operate and interact with the robots. Graphical user interfaces (GUIs) are one type of interface, whereas immersive interfaces, such as teleoperation and VR-based control, are another.

Data Analytics

Data analytics methods are used to evaluate robot soccer teams’ play and find ways to enhance their play. Team strategies, individual robot performance, and the overall efficacy of the games can be gleaned from the data acquired during matches, such as robot positions, ball trajectories, and game events.

Robot soccer is an exciting and competitive sport that pushes the limits of robotics, AI, and autonomous systems thanks to the combination of these technologies. Progress in these areas is essential to the development of robot soccer as a research field and a tool for advancing robotics and artificial intelligence education.

Specific machine learning algorithms used in robot soccer?

While these components are without a doubt necessary, robot soccer also uses some machine learning algorithms, which has helped them make the progress they have today. Below are some of them.

Reinforcement Learning

The sport of robot soccer has been taught to robots using reinforcement learning (RL) algorithms. In RL, a bot learns to take successive actions by observing its surroundings and responding accordingly. RL can be used to teach robots new tactics, routines, and rules of engagement in the sport of robot soccer. With RL, we can teach a robot how to move and position itself so that it has the best possible shot at scoring or preventing goals, for instance.

Deep Learning

Several sensory tasks in robot soccer have been tackled with the use of deep learning methods, most notably convolutional neural networks (CNNs). Convolutional neural networks (CNNs) can take in visual data like images or video frames and produce useful features from them. CNNs can be used to estimate the location and orientation of objects in the robot soccer environment, as well as to detect the ball, identify teammates and opponents, and recognize faces. The application of deep learning algorithms for action recognition has greatly enhanced robots’ ability to comprehend and react to happenings in games.

Genetic Algorithms

Several aspects of robot behaviour and strategy in robot soccer have been optimised with the help of genetic algorithms (GAs). To find the optimal answer, GAs iteratively apply genetic operations like mutation and crossover to a pool of candidates. In the context of robot soccer, GAs can be used to fine-tune the controllers’ parameters and weights. They are also used to predict the robots’ movements and evolve the teams’ cooperation and decision-making tactics.

Q-Learning

Q-Learning is a prominent reinforcement learning algorithm in robot soccer. It can be used to learn how to select the best possible actions given a given value function. In Q-learning, an agent learns a Q-value for each state-activity combination, where Q is the cumulative expected reward for taking that action in that state. The agent can learn a good decision-making policy by exploring and receiving rewards in order to update the Q-values. Robot soccer teams can use Q-Learning to figure out how to get around, handle the ball, and pass the ball most effectively.

Evolutionary Strategies

Evolutionary strategies (ES) are a type of optimization algorithm that takes cues from the way evolution works in the natural world. In ES, a population of potential solutions is maintained, and then selection, recombination, and mutation are applied iteratively to create superior solutions. ES can be used to fine-tune the performance of robot controllers, behaviors, and strategies in the sport of robot soccer. Robots can learn to select the ideal passing targets based on the game circumstances and the position of teammates and opponents with the help of ES, which can be used to build effective passing methods, for example.

How do The Robots Communicate With Each Other in Robot Soccer?

When playing robot soccer, the robots must be able to talk to each other and coordinate their moves. This way, robot soccer will look more like a real soccer match. The robots are able to talk to one another using a variety of ways and protocols. FuturePlay News provides you all the typical methods robots communicate with each other in robot soccer which are as follows:

Wireless Communication

In many robot soccer competitions, robots talk to each other via wireless communication protocols. To share information, each robot has a wireless transmitter and receiver. Radiofrequency (RF) and Bluetooth are two of the possible methods of communication.

Local Area Network (LAN)

Local area networks may be used by robot soccer teams as a means of communication. The robots are all linked up to the same network, where they may freely trade information and messages with one another. This method paves the way for more stable and high-bandwidth robot-to-robot communication.

Multi-Robot Systems Frameworks

The communication infrastructure and protocols used by some robot soccer teams are provided by multi-robot systems frameworks. These structures allow for the communication between robots on where the ball is, what has happened in the game, and the robots’ positions.

Shared World Models

The robots in a shared world model game cooperate by agreeing on a common model of the game’s state. Each robot contributes to this model by adding perceptual information it has collected and then sharing it with others. The robots are able to work together to make judgments and modify their play based on a shared knowledge of the game world.

Robots in a game of robot soccer must be able to effectively communicate with one another in order to carry out their strategies, coordinate their movements, pass the ball, and react to the actions of their teammates and opponents. It enables the robots to cooperate, elevating the game’s level of difficulty and excitement.

Sport Enthusiast, Builder of brands, and proud founder of Machina Sports, dedicated to pioneering the fusion of human athleticism with cutting-edge technology. Committed to creating a global platform and brand that celebrates the excitement and innovation inherent in Machina Sports while engaging a diverse community of enthusiasts and athletes worldwide.

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Matches in the National Havoc Robot League Have Reached a New Level

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The 2024 National Havoc Robot League Season (NHRL) is powering along as 3lb, 12lb, and 30lb combat robots from across the globe battle to become NHRL’s 2024 World Champion. 

Robot combat is the most popular Machina Sport in the world and is also the most accessible. The sport is simple: two robots enter the cage, and only one leaves. The goal is to destroy the opposing robot and render it unable to continue to battle.

Robot combat has been around since the 90s with popular leagues like Battlebots and Robot Wars. The NHRL is continuing this tradition with seven yearly events held in the House of Havoc, located in Norwalk, CT. The NHRL is now the biggest robot combat league in the world, with shows selling out and hundreds of thousands of fans around the world tuning in to broadcasts.

Builders use cutting-edge technology and ground-breaking ideas to create new and unique robots that are capable of destroying an opposing robot within seconds. The NHRL offers fantastic cash prizes, and the world champions get to lift the coveted Golden Dumpster trophy.

So far, two NHRL events have taken place, and there are still five more to go. Let’s look back at what has transpired this season and see which robots are likely to become world champions!

The First NHRL Event of the Year

The 2024 NHRL season started with a bang on January 20, 2024, with a special event reserved solely for robots who had never competed in the NHRL before. 3lb, 12lb, and 30lb bots took part in the event, and the top four from each category received an invitation to the 2024 World Championships, which will feature a $50,000 prize pool.

There were some incredibly designed robots at the event, from flamethrowers to saws to classic flipping bots and even one very nasty robot armed with a nail gun. 

As there were many new robots competing, there were a healthy number of malfunctions. Some robots just stopped working, while others exploded. Bots were able to win matches by simply surviving.

The 12lb competition was by far the most competitive. Eight fiercely designed bots entered the cage and did their absolute best to destroy their opponents, but only one emerged victorious: Questionable Choices. 

This compact robot is built low to the ground and is armed with a buzz saw in the middle. Then, just below the buzz saw are two sharp spikes that Questionable Choices is fond of ramming into its opponents. The lighting-quick robot also can get underneath opposing bots and flip them high into the air.

In the final, Questionable Choices ended up destroying the drive of Blue Marlin, a similarly designed robot, and exerting superior control.

In the 3lb bracket, Scurryfest was by far the best robot and cruised to the finals, where it defeated Repeater. Then, in the 30lb bracket, Moccasin crushed Colossal Avian.

NHRL Brings the Heat With Its Second Event of the Season

The second NHRL event of the year took place in March, and again, 3lb, 12lb, and 30lb battle bots were competing for a place in the World Championships. The 3lb final between Red Panda and Eruption was a particularly fiery affair, with sparks literally flying.

Eruption came into the fight with a 51-26 record and didn’t disappoint. Channeling Mike Tyson, Eruption launched itself at Red Panda and, within the opening seconds of the fight, had Red Panda upside down and helpless. After continuing to violently ram Red Panda over and over again, Eruption secured the victory.

It only took a little over one minute for Pramheda to KO Black Jack and win the 12lb title. In the 30lb final, defending world champion Emulsifier managed to flip Vorion over. Then, while Vorion was in a vulnerable position, Emulsifier crept and drove its buzz raw right into the stranded robot. Smoke flew out of Vorion, and that was the end of the fight.

The next NHRL is on April 20 and there will be events running monthly up until October. This will be followed by the NHRL World Championship held in November, which is a can’t-miss event as the best battle bots throw down to see who is the best on the planet!

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Robots on the Court? AI Ball Machine Heats Up Tennis Training

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Tennis Bll on Blue Hard Court

Ball machines are nothing new in tennis and have been around since the 1970s. However, machines that can mimic players and are powered by AI are. Volley, an innovative sports tech company, has developed a cutting-edge training device that can be used for virtually all racquet sports, including tennis, padel, and pickleball.

Instead of just lobbing balls to you at different velocities, this new-age ball machine actively analyzes your gameplay and then unleashes balls. The goal of Volley’s training device is to recreate gameplay and give players the exact type of feeding they require to improve. 

Volley’s Ball Machine Is Tech Heavy

This AI-powered ball machine comes loaded with three cameras. One camera closely tracks the ball, while another camera tracks you, recording your every stroke. The goal is to get you hitting forehands like prime Roger Federer. The third camera is located within the ball machine and gives customer support a view of exactly what’s going on in case of issues.

Volley’s creation also comes with an LED screen where you can program different workouts. The ball machine is completely adjustable, so it’s capable of hitting booming first serves as well as gentle drop volleys, and everything in between. The training device stands at 87 inches and can tilt, twist, and rotate, ensuring the ball can be launched all over the court.

You can also download the Volley mobile app, where you’ll find workout plans and different statistics and even watch yourself hitting. Another cool feature is the remote control element. Directly from the app, you can instruct the ball machine exactly where you want to bounce, allowing you to work on specific elements of your game.

Volley hopes to ditch the remote control element and allow players to instruct the ball trainer via hand gestures. The team is constantly looking for new features they can add to the ball machine.

AI Ball Machine Sells Out in Less Than Four Months

It didn’t take long for Volley’s ball machine to grow an avid following within the tennis community. It was released in September 2023 and, within a couple of months, sold out. You can find Volley’s trainer at 45 different tennis clubs dotted around the US, including in NJ, FL, MA, OH, and PA. Volley costs a pretty penny and is currently being leased by tennis clubs for up to $3,000 per month.

Racquet sports technology companies are booming as they scramble to meet the needs of players who are no longer satisfied with a static ball machine. Slinger is another smart ball machine that can track your shots and offer stroke advice, while Proton comes loaded with sensors, resulting in laser-accurate ball delivery to all parts of the court.

Volley believes it can serve the needs of pros and hobbyists who are looking to recreate gameplay. With the help of AI, the company hopes to offer a next-generation solution similar to golf simulators.

Volley’s trainer is particularly effective for paddle, which traditional ball trainers have ignored. Volley’s ball machine can create common shots in paddles, which standard trainers can’t. Expect to see this new style of ball machine popping up at tennis clubs all over the world very soon.

Volley’s Ball Machine Can’t Hit the Ball Back

Despite being powered by AI and having the skills to recreate all of your favorite shots, Volley’s trainer can’t engage in rallies. There are currently no commercially available tennis-playing robots as the technology just isn’t there yet. However, a team at Georgia Tech has built a robot that can sustain rallies.

ESTHER features two wheels and an arm that holds a tennis racquet. The wheelchair-designed robot plays tennis at a very low level and is a fan of hitting high-looping shots. However, in a feat of engineering ingenuity, ESTHER can use its cameras to identify where the ball is going to land, move into position, and then strike it. More often than not, the ball lands in the court.

Tennis robots are advancing quickly. It’s reasonable to think that within 30 years, there’ll be a robot that can defeat the best pro tennis players. Also, local tennis coaches may be a thing of the past, with players opting to be trained by robots.

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H1 Robot Is Now Faster Than Boston Dynamics’ Atlas, Reaching 7.38 Mph

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Humanoid Robot Sprinter in a track

Unitree Robotics, based in Hangzhou, China, has smashed the robot speed record. Their humanoid robot, dubbed H1, is two miles per hour faster than Boston Dynamics’ Atlas robot and can reach impressive speeds of up to 3.3 meters per second. 

H1 won’t win any points for his running style, but the speed shuffle is effective, and he’s currently the fastest humanoid robot on the planet. The robot clocked in at just under 7.4 mph, which was achieved on a concrete pavement. The previous record was set by Atlas at 5.59 mph. H1 even broke the record while wearing pants! 

Meet China’s Answer to Atlas

Build-wise, H1 is comparable to a very slender human, standing at 71 inches tall and weighing just 100 pounds. The robot is loaded with 3D sensors and a depth camera, which gives him superhuman vision.

This Chinese robot has a completely hollow torso where you’ll find all of the electrical wiring, which acts as H1’s veins. While H1 does have arms, the humanoid robot is currently running around without hands. Unitree Robotics is trying to build hands that can rival Atlas’. The robot won’t be very effective if it can’t lift and put things down.

While Unitree’s H1 robot isn’t nearly as advanced as Boston Dynamics’ Atlas robot, the Chinese company is trying to compete on price. You can purchase H1 for as little as $90,000, while Atlast starts at $150,000 for the very basic version.

There’s a huge demand for humanoid robots as companies all over the world dream of employing them to take over every role imaginable, from mechanic to nurse to builder. However, we’re a long way away from automated workplaces staffed by H1s and Atlases.

What Can H1 Do?

H1 shows incredible balance. In a recent video, it was shown being kicked repeatedly from different angles, but the robot was consistently able to adjust its footing and never came close to falling over. The robot also could sense the tester near him and briefly paused before continuing to walk.

This Chinese robot has an odd gait with overly bent knees, but it’s proven effective. H1 can navigate stairs like a pro and even walk down backwards and while spinning. There are numerous people who would struggle with that feat!

After the engineers attached hand molds, the humanoid robot can now pick up certain objects and hold them securely. It’ll be interesting to see what the robot can do when it has fully functional hands.

H1 can jump as high as the average person and was even out jumping the tester. The Unitree Robotics creation even displayed impressive all-body coordination by dancing. Both arms and legs were moving at the same time and keeping up the beat. While the little jig won’t win any dancing competitions, it shows the robot is developing quickly.

In an even more incredible feat, the Unitree robot performed a standing backflip and stuck the landing. It didn’t leap very high in the air and stumbled a bit, but still, it remained standing.

Not Quite on Atlas’ Level Yet

Boston Dynamics’ Atlas is the gold standard when it comes to humanoid robots. Atlas can complete an obstacle course faster than most people and looks natural doing so. This American robot has a far more natural gait than H1 and a higher level of coordination.

Atlas can do backflips with a tuck, performing the challenging movement multiple times in a row and landing perfectly. Recently, the Boston Dynamics’ robot displayed its skills on a mock building site. Atlas was able to pick up a heavy tool bag and then clamber up multi-story scaffolding, and safely deliver the bag to the builder at the top. Then, in style did a flip off a box to celebrate his performance.

H1 may be the fastest humanoid robot on the planet, but it still can’t perform many basic tasks, especially without functional hands. The Atlas robot is far more nimble, as evidenced by its parkour tricks. It also demonstrated an ability to pick up and move heavy objects, tasks that could transform the manufacturing and building industry. Currently, neither of these humanoid robots can sprint, but it’s only a matter of time before they break the 100-meter record, too!

Read More: Cassie – The Fastest Bipedal Robot

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