30 Apr

Logics Project -- RoCo + Webots

Chang Liu computational design

Goal 1: Get RoCo-designed robots to work well in Webots

Problems:
  1. RoCo generated .stl file is for presentation, the parts are considered as one solid body, which means there will be no rotational/translational parts simulated in Webots.
  2. How to get RoCo generate files that can differentiate the assemblies?
  3. How to add axis (joints) and motors?
Potential solutions:

Two tutorials that may be useful:

  1. Exporting Autodesk Inventor CAD models to Webots

Summary (Potential solutions, need validation):

  • In RoCo, Save robot assembly into .obj file.
  • In Blender software, convert .obj file into .wrl file.
  • In Webots, import .wrl file. There should be several "Shape" representing different components in the robot assembly designed from RoCo.
  • In Webots, add a new base node (robot), move all "Shape" into "children" under the new base node (robot).
  • (Optional) In Webots, save the new base node (robot) into .wbo file, for future multi-agent / SWARM applications.

Here are the file format that Webots can import. I assume if we can export RoCo design into one of these format (also makes discrete component move independently), we can import directly into Webots.

  1. Webots Adding Joints to Robot

Following last step, here is a summary (Potential solutions, need validation):

  • TBD

Intermedium results

1. Import .stl robot car and drop it in Webots from different height. {To learn how to use Webots?}
  • Drop from 0.05 height

  • Drop from 0.25 height

  • Drop from 0.35 height

Problem:
  1. Car passed the ground.
  2. Car wheels in the ground at the end for some cases.
Potential reasons:
  1. Bounding properties are not set correctly (currently approximated to simple shapes).
  2. The wheels are too thin to be captured (currently using paperbot design).
  3. It may be necessary to manually add a ground.
  4. Contact properties are not set correctly.
Tryouts:
  1. Different bounding types, rect, cylinder, sphere, etc do not work well, too much simplification. Looking at the 'meshing' on the robot car, it should be correct using the current settings.
  2. Using 3mm plywood robot car, same/worse simulation results.

  1. Based on a little research online, "Grounds can be defined using the Plane or the ElevationGrid primitives." So I manually added a ground using plane.

Well, same behavior, sad....

  1. Here is a description of how to set up contact properties. Well, I changed bounce from default 0.5 to 1. Well, it still does not look correct.... I will keep debugging ...

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