Robotics is evolving rapidly. Whether it’s drones that map forests, robotic arms that build cars, or swarms of warehouse bots — all of them rely on real-time decisions, secure networking, and smart sensor fusion.
That’s where Pepper, Pantherun’s open-source FPGA development board, comes in. More than just a platform — it’s a playground for robotics innovation.
Real-Time Motor Control
Traditional microcontrollers can’t guarantee jitter-free performance for motor control. Pepper, powered by FPGA logic, delivers precise PWM signals and fast encoder feedback in deterministic time.
Code Snippet: Custom PWM Generator in Verilog
module pwm_generator(input clk, input duty_cycle, output reg pwm_out);
reg counter;
always @(posedge clk) begin
counter <= counter + 1;
pwm_out <= (counter < duty_cycle) ? 1 : 0;
end
endmodule
Applications: Servo control in arms, quadcopter thrust balancing, AGV wheel synchronization
Vision and Sensor Fusion
Robots need to see — and think — in real time. Pepper processes images and sensor data in parallel hardware pipelines, enabling:
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- Stereo vision disparity
- IMU + LIDAR sensor fusion
- Real-time object detection prep for edge AI
Snippet: Sobel Filter on FPGA (high-level SystemVerilog sketch)
// Line buffer and kernel multiply for edge detection
logic signed Gx, Gy, magnitude;
Gx = (I[x-1][y+1] + 2*I[x][y+1] + I[x+1][y+1]) – (I[x-1][y-1] + 2*I[x][y-1] + I[x+1][y-1]);
Gy = (I[x-1][y-1] + 2*I[x-1][y] + I[x-1][y+1]) – (I[x+1][y-1] + 2*I[x+1][y] + I[x+1][y+1]);
magnitude = sqrt(Gx*Gx + Gy*Gy);
Applications: Autonomous navigation, real-time SLAM, terrain mapping
Secure Robotic Communication
Snippet: Secure Packet Transmit Flow
[Sensor Data] --> [Encrypt in AES Block] --> [Tag with HMAC] --> [Transmit via UDP over FPGA stack]
Applications: Military drones, remote surgery bots, robot fleets in secure zones
AI at the Edge
Pepper can accelerate machine learning models on its programmable fabric — making local inference fast, low-power, and privacy-safe.
Code Snippet: TinyML model using quantized weights in hardware
// Multiply-accumulate unit for a convolution kernel
output logic signed result;
always_comb begin
result = (input1 * weight1) + (input2 * weight2) + … + bias;
end
Applications: Gesture recognition, obstacle classification, anomaly detection
Swarm Robotics and Networking
Pepper includes a hardware-accelerated L2/L3 network stack, optimized for sub-microsecond latency. This enables:
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- TSN (Time Sensitive Networking)
- Real-time coordination between swarm units
- Fault-tolerant routing on-the-fly
Snippet: Deterministic UDP Stack Overview
[Sensor Reading] → [FPGA MAC Layer] → [IP Packetization] → [UDP Tx Buffer] → Ethernet PHY
Applications: Robot swarms, factory automation, distributed search & rescue
Robotics Education and R&D
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- ROS2 compatible
- Free webinars every 2 weeks
- Full VHDL/Verilog & C support
Starter Project: ROS2 Node Offloading Sensor Read to FPGA
// Publish IMU data from Pepper’s hardware FIFO buffer
sensor_msgs::Imu imu_msg;
imu_msg.linear_acceleration.x = read_fpga_fifo(IMU_X);
imu_pub.publish(imu_msg);
Applications: Autonomous robotics courses, thesis projects, rapid product prototyping
Why Choose Pepper?
The Future of Robotics is Reprogrammable
Pepper isn’t just another dev board. It’s a powerful gateway to the future of robotics — one where hardware is programmable, communication is secure, and AI is local.
Whether you’re building the next drone fleet, an autonomous rover, or a surgical robot, Pepper gives you full control over your compute stack — from the transistor up.
Ready to Build Your Robot with Pepper?
Visit: https://pepper.pantherun.com/
Apply for the 100 Robots Giveaway for educators and makers.
About Pantherun:
Pantherun is a cyber security innovator with a patent pending approach to data protection, that transforms security by making encryption possible in real-time, while making breach of security 10X harder compared to existing global solutions, at better performance and price.
