03 Aug

433 MHz Packet Radio Stacks: an easy to use frequency-diverse mesh network

Bhavik Joshi networking mesh mesh network 433mhz radio frequency diversity

This week, our team of summer undergrads began planning the integration of all of our projects together into one big integrated demo. So, I had to start making my networking code and hardware easy to add into and use in my peers’ projects. To do so, I switched from using the small 433 MHz packet radio breakout boards which were connected by wires to the 433 MHz Packet Radio Featherwing stack boards. Using these boards on top of our ESP32 feather boards, I made a network of three nodes that communicated to one another using both 2.4 GHz and 433 MHz simultaneously, and displayed the two way delay on the dashboard. Here is a video of the demonstration:

As you can see, the one node that is the MQTT bridge talking to the dashboard (the one on the right) is able to individually communicate with either node, and displays the delay onto the dash.

The biggest difference of this demo from last week’s is the fact that the IDs of the radios aren’t hardcoded in the header file anymore; now, when I begin the MQTT connection, the “master” node starts assigning every node currently connected over painlessMesh a unique radio id. If a new node joins the network, it gets assigned the next number. The cool part of this is that even if the master node gets disconnected from the network after the set up, the other nodes will still be assigning new nodes the next id number. This adds further robustness to the system since the robots will still be able to operate somewhat normally if any other robots go offline. Each node also gets a synchronized table that matches the radio ids to the painlessMesh ids (which are derived from each board’s MAC address).

Another interesting point that I wanted to bring up this week was choosing which antenna to use on the radio featherwings. Last week, a lot of the antennas that I made out of the solid core copper wire kept snapping since they’re so brittle. Today, I tried using a stranded wire instead. Here is a picture of a stranded wire antenna (left) and a solid core antenna (right).

antenna%20difference

The stranded wire is a lot floppier, so it won’t snap off if it gets brittle from too much movement; however, it does seem weaker and that it can be ripped off easier. There may be a benefit to using one or the other on specific robots depending on their environments and movement, so that is something to explore in the future.

Next Post Previous Post

Search

Popular Tags

foray origami robots blimp ducky vr edg SLAM computer vision computational design roco slam Origami rocolib arduino RoCo design Robot Compiler simulation Motion Planning autonomy edg-model mesh reinforcement learning; multi-agent; mean-field; logics create; kalman filter; motors imu friction mesh network tank esp32 networking contacts multi-agent pushing auxetic machine learning dynamics esp now visual processing wireless control communication webapp pcbs structure ROCO i2c path planning cars Robotica reinforcement learning computer skill openmv test motor dataset visual DARPA odometry Battery surface structure power consumption dash port-arrays modeling origami paper frequency diversity semantic infrastructure auxetics feedback gondola machine vision Consensus bistable auxetic controller gestural intuition mixed reality unit cell robot crawler gestural interface open house network create; kalman filter; EM; refactoring 4-wheeled darpa; create; icra2021 apriltags lidar logic control FORAY perception Quantum Information crazyflie 433mhz optimization propeller Foray structures sensors environmental design radio DER Kullback-Leibler Divergence lasercutter tutorial von Mises Fisher Distribution IR simulink legs project management localization distributed optimization blynk Auxetics painlessmesh experiment results offline reinforcement learning exam research proposal altimeter thrust PCB sensor fusion gate or webots multi-agent path planning UAV RL integration test arnhold joystick balloon test vision processing pushing problem modeling contacts mesh networking motor configuration nodemcu swarm object detection fusionbot ros2 rviz2 gazebo conference matlab arduino nano decentralized surfaces keyboard IROS Integrated Demo throughput peripheral device paper review reinforcement learning; motorized boats Writing slam; paper review rhino Short Post range distance surp origami robot nylon pvdf elastic polymers plastic hot plates reinforcement learning; multi-agent; mean-field; game theory; mean field; reinforcement learning current reinforcement learning; multi-agent; Xbee Zigbee robot compiler spi contact neural network dynamic modeling quasi-static modeling edg-examples Kalman Filter port-array von Mises Fisher von Mises-Fisher literature review multipack car Zero Elastic Energy modes swarm control solar cars system ID serial monitor edg-ide Discrete Elastic Rods ceres debug Consensus Algorithms coriolis force dynamic model proposal wtf deep learning pdms interpolation wireless signal strength reinforcement learning; multi-agent; decentralized misc reinforcement learning; multi-agent; mean-field; stationary demo video flytrap papers steering structured field pivoting differential power detectionrate color shape edg-refactoring pain python vision Machine Learning Q-Learning Model-Free release conferences quasi-static pushing liquid crystal elastomer nonholonomic systems iot grant feedback-controlled controls trajectory optimization surface contact Characterization tool ML multi-agent path finding Liquid Crystal Elastomers Learning kinematic_model reservoir computing 2.4 GHz concept 433 MHz Bayesian Network creative Computer skill internal silicone integrated system instance segmentation marginal cost pricing bootstrapping Bayesian networks ensemble methods offline Rl hardware manipulation COVID 19 paper cutter ESP32 kalman filter; multi-robot state space delta tolling buoyancy chiral square unit cell pressure distribution model prediction thrust configuration grid fabrication firmware protobuf #activecapturingmechanism #claw 3D-CHESS; kalman filter boxboat tracking model-based RL von Mises Distribution slam; flowchart; spring-mass 3D-CHESS; 433 rssi signal strength multirobot system writing in-situ design buoyancy equations boat deployment cell cameras der newton's method Barycenter Euler-Bernoulli beam sma-based actuator game theory Bessel functions von Mises-Fisher Distributions ide HDL board design battery current sensor ZMP center of friction Multi-Agent Systems code balloon semantic segmentation MIMO communications ICRA ICML guassian processes probabilistic neural networks graph generators MBRL batch learning ros edg-compiler multirobot optitrack adaptive control motor driver submission DNN kalman filtering point cloud 2.4ghz latency scenic codesign presentations machine training preliminary exam multiple devices inkscape material multi-object tracking ICLR Python actuation catching mechanism integration flapping color_segmentation lab_data result segmentation on multiple object and centers finding competition prelab multiple blimps pid defender Synthesizable Radio 3d printing kalman filter motor distribution barometer detection lift force depth estimation cad solidworks design tool pixracer pixhawk ardupilot flight controller regression fly test sensor selection