Autonomous crop production has arrived. Introducing FAMA, our autonomous farming robot. It plants seeds, applies fertilizer, and navigates without GPS — built for conditions where traditional systems struggle. We’re changing how farming gets done, one robot at a time. 🌽🤖

robots are cool man. even cooler when they’re out there fighting one of civilisation’s oldest problems: feeding everyone. more robots in the fields pls

delta robot kinematics can look intimidating at first, but the underlying geometry is surprisingly intuitive once you understand how the mechanism is constrained. the core problem is inverse kinematics: given a desired end-effector position, determine the three motor angles required to reach it. each motor rotates an upper arm about a fixed axis. that rotation determines the position of the corresponding elbow. the lower linkage has a fixed length, which constrains where the end effector can be relative to that elbow. for each arm, we solve that geometric constraint for the required joint angle. since the three arms are arranged 120° apart, the same relationship is applied in each rotational plane to obtain θ₁, θ₂ and θ₃. that gives us the joint commands needed to move the end effector to any reachable point in the workspace, while respecting the robot's geometry and limits. we've wrapped this kinematics and motion layer into a C++ API that gives higher-level software a clean interface to the robot. that becomes particularly interesting when you put an LLM on top of it, allowing human language to become another way of commanding and programming the robot.

delta robots are one of my favorite typa robots to tweak with nearly endless applications across ag and food processing: zero-chemical weed removal, harvesting, material handling, etc slap an intelligence layer on top, let humans program them in plain english instead of machine language, and u kinda unlock the whole thing old video below but we got a couple of delta-powered products cooking. serving hot soon moar robots moarrrrr

Endless possibilities in the food and ag industries with robots! More robots incoming. 🤖🦾

delta robots by @3farmatebots for high-speed pick-and-place operations. from autonomous field operations to robotic fruit harvesting, the possibilities are expanding; and we’re only just getting started.

Cool robots do cool jobs 🤖🌽

our robots are not just cool but engineered to democratize precision farming, independent of GPS modules and satellite-based systems that cause multipath errors in uncontrolled scenarios and resource-limited settings. it is our objective to reduce operational costs and make the planting process more convenient, efficient, and cost-effective for produce growers around the world.

wait to see what we do to these in the coming weeks :))

If you’re building robots for the physical world, every assumption eventually meets reality. There is no substitute for real-world testing, and we learned that the hard way. The field has a way of exposing the gap between what you believe should work and what actually does. When we started @3farmatebots, our mission was simple: to automate farming. As straightforward as that sounds, every decision we have made since has revolved around balancing three competing objectives. The robot has to solve crucial labor problems, it has to do so reliably under real farming conditions, and it has to be affordable enough that the very people it is built for can actually adopt it. Achieving all three simultaneously is where the real engineering begins. One of the most valuable lessons we learned came from one of our earliest field tests. We had spent weeks refining a new iteration of the platform, convinced that we had addressed many of the shortcomings from previous versions. On paper, everything made sense. In the workshop, everything behaved as expected. But the moment we drove the robot into the field, reality exposed flaws that no amount of bench testing could have revealed. The soil had been prepared exactly as farmers across Ghana prepare their land every season. It wasn't unusual terrain. It was the terrain our robot was supposed to operate on every single day. Yet almost immediately, we realised the platform simply didn't have enough traction or usable power to traverse the field while performing its intended task. It wasn't a software bug or a faulty component. We simply designed around assumptions instead of evidence. That single afternoon changed the direction of the next iteration. We redesigned the drivetrain, reconsidered how power was distributed throughout the system, and substantially increased the available torque. When we returned to the field, the difference was immediately obvious. The robot not only handled the terrain confidently but also had enough power in reserve to execute its work reliably under the same conditions that had defeated it only weeks before. Moments like these are expensive. They consume weeks of engineering effort, demand resources that are difficult for a young company to spare, and occasionally force you to admit that an idea you believed in simply doesn't work. But they also produce something far more valuable than confidence. They produce evidence. Every failed test removes another assumption. Every iteration brings you closer to a system that isn't merely impressive in a demonstration but dependable where it actually matters. There is no shortcut around that process. If you're building for the physical world, reality will always have the final say. The sooner you expose your ideas to it, the sooner you begin building products that can genuinely withstand it!

We believe the farmer of the future is one who moves up the stack, away from physically working the fields and towards planning, managing and executing farm operations. For that to happen, agriculture needs a new kind of workforce. One that is reliable, capable and scalable, and one that does not require a human to be physically attached to every task. This is the workforce we at 3Farmate have committed years to building. It is robotic, autonomous and designed to make crop production more scalable and cost-effective, bringing us closer to a future of food abundance. Our CEO, Clinton Anani (@oxncgen) , wrote the full manifesto on what this future looks like, where it comes from and why we believe the next agricultural revolution is already beginning. Read it here: 3farmate.com/blog/the-farmer… #AgTech #Robotics #AutonomousFarming #FutureOfFarming #AI #FoodSecurity

Just get started. You'll figure it all out later.

Vision-native autonomy means FAMA robots can be deployed anywhere without requiring specialised infrastructure! 🤖🌽 #agtech #AI

our vision-native autonomous driving stack unlocks a new kind of farm mechanisation that is scalable and accessible in literally anywhere in the world or the solar system without requiring specialised positioning infra. by removing the dependency on RTK-GNSS, autonomous machinery becomes viable in places where conventional ag autonomy simply cannot operate economically or reliably. that matters at both ends of the farm. it can make precision mechanisation accessible to smallholders who could never justify expensive infrastructure, while giving large farms a more flexible way to deploy autonomy across fragmented or infrastructure-poor fields. and the real unlock is fleets. once perception, planning and control can run reliably at the edge, dropping dozens or hundreds of autonomous machines into the field starts looking less like deploying tractors and more like deploying a distributed robotic system. cheaper, more capable edge compute is only going to accelerate this

actuator selection is one of those things that looks deceptively simple until you actually have to build the machine. when we first started working with geared motors, wiper motors were our immediate pick. they were a pain to work with from the get-go, but as FAMA grew in size and complexity, we needed more torque and had few other options. at the time, we moved to a 12 V architecture and used four geared wiper motors, one at each wheel. each motor was rated at around 60 W, giving us 240 W across the drivetrain. with roughly 8 Nm of output torque per motor, the four motors gave us about 32 Nm of combined wheel torque. with our 22 cm diameter tires, that translated to roughly 291 N of theoretical tractive force at the ground. for a robot weighing under 50 kg at the time, that was enough to get us moving. on paper, everything looked good. we had the torque, the power and a robot that could actually move. we thought we had finally figured out the actuator problem. then the real problems started biting us in the ass. we kept blowing motor controllers. the motors were inductive loads, and rapid current changes during PWM switching and braking produced large voltage transients from back EMF and the energy stored in the motor windings. our early boards weren’t designed to absorb those spikes, and components kept failing. a lot has changed since then, but those were some hard lessons to learn!

throwback to when we had our very first real-world test with our 5th FAMA prototype in 2022. this was our very first ever build with metal, and it was scrappy as hell, but it gave us a platform to finally put our autonomous driving stack to the real-world test. for months, we were locked in a garage figuring out how to deal with metal: welding, cutting, grinding, riveting, etc. the funny part was that we were still software engineers with basically zero experience building anything in the real world. we knew getting it right on the first try wasn’t possible, so we aimed for smaller goals with each prototype, rapidly testing, breaking, learning, and scaling from there. i still remember watching youtube videos on how to weld. we went through several prototypes, learned a ton, and gathered massive amounts of field data that shaped how we thought about FAMA and its ability to understand and operate in the field. that period completely changed how we approached product development. we became obsessed with field testing because we realized the fastest way to get better was to put something into the real world, see what broke, learn from it, and build the next version better. looking back, those scrappy days taught us a lot about engineering, resilience, and building with almost no idea what we were doing at first. we were just determined to prove that our idea could work, so we had to learn everything on the fly and keep going. a lot has changed since then, but those years of prototypes, failed tests, broken parts, field experiments, and endless iteration are still baked into how we build today. i lowkey miss the scrappy hacking days :)

contrary to what many may believe, when robots completely abstract the execution layer of farming, the farmer doesn’t become irrelevant. a new kind of sophisticated farmer emerges. food production becomes dependent on how well the farmer architects a farming operation: what to grow, where to grow it, when to plant, how to allocate resources, and how to respond to changing conditions. that requires knowing and understanding the farm better than anyone else.

with fleet farming made possible by FAMA robots, industrialized agriculture takes a completely different path - one of extreme scale at ridiculous cost savings. in the end - food abundance for all. this is the way

prophesying: FAMA fleet robots growing corn on Mars. @elonmusk i said it first