24 Jun

RoCo: extend the behavior to enable more analysis of our designs

Chang Liu computational design

RoCo demonstration brainstorming

We came up with some challenging problems for RoCo demo and we will keep brainstorming. (Thank you Nabeel and Mauricio) Here are some ideas (currently focusing on Paperbot, will extend to other robots later on):

  1. Robot going through large ridges or bumps: robot will get stuck on a ridge if wheel diameter is not large enough or if the robot is too wide.

  2. Robot following path with sharp turns: smaller wheel diameter is needed to follow precisely, larger wheel diameter is needed to finish the task on time.

  3. Robot going up or down a very steep incline: need certain wheel diameter to maintain speed and torque, certain mass to control friction, but large wheels will cause the robot to tip.

  4. Battle bots/ramming another robot: need high mass but low friction to acceleration to a high momentum (optimizing momentum, wheel radius is also a factor) [I need more time to think about this one, it sounds interesting, but I need to think through]

Project overview

I also talked to Pehuen, we agreed on how this research will be carried out and Pehuen will help with the optimization part of the problem. The basic logic is (this is also for Nabeel to catch up with the progress): We have RoCo take design parameters (e.g. w, l, h, etc) as input, expand the functionality of RoCo to generate 3D ready-to-simulate design file (the more I think about it, the more I feel like we need generate a file can be applied not just to Webots but other simulation tool as well, so I think .obj will be our best option.) Once we have the automatic generated file, we will apply that to Webots + some solid environment (no Scenic involved at this stage). Output from Webots (e.g. v) will be grouped with RoCo input (e.g. w, l, h, etc) as a set of input-output-relationship. We will be some (maybe 10-100) sets needed in order to build an input-output-relationship so that we can give the best redesign suggestion from performance. I will work on automating RoCo to generate ‘simulatable’ robots, and Pehuen will work on the optimization part later on.

I have also talked to Yusuke regarding my current progress, so that Yusuke can carry on the Webots simulation part of the problem.

RoCo implementation questions

  1. In Paperbot.yaml, the paperbot is considered as one piece. Is this understanding correct?

Paperbot.yaml

  1. In function STLWrite(faces, fp, kwargs), the faces contain all parts, body and wheels. Do you know if there is an easy way to differentiate the body and wheels? (I know this is do-able since it is separated in DXF 2d drawing, but I am having trouble finding where it happens)

  2. Can we split different body part in the ESPSegBuilder.py? We will still have a whole body ESPSeg.yaml saved to library in the end, but adding different components along the way, e.g. body.yaml, wheel.yaml. Do you think this is a valid approach?

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