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ooqp_eigen_interface

Eigen interface for the OOQP solver library

path_smoother

smooth path/curve with Gradient Descent method

pathplanning

Common used path planning algorithms with animations.

pidcontrol

planning-rgrrt

This was the project I did under Prof. David Meger. Abstract: Even the simplest robot model is subject to differential constraint. In robotics most problems involve differential constraints that arise from kinematics and dynamics of the robot. In order to plan a collision free path for robot which it can successfully follow its important to consider these constraints while planning. This project studies a very popular randomized path planning technique called RRT-Planning and apply it for a simple robot model with differential constraints. In order to deal with differential constraints a new sampling technique is studied in the report which is based on feasibility sets. Report also discusses in details theory of reachability sets and discusses reachability sets for simple car motion. Properties of RRTs and how the differential constraint problem affect those properties are also discussed in the report. Finally report also discusses the scope of improvement in the studied algorithms.

potential-ilqr

probabilistic-robotics

《概率机器人》书和课后习题

probabilistic_robotics

solution of exercises of the book "probabilistic robotics"

psins-develop-group

PSINS toolbox

pycubicspline

Cubic spline library on python

pylib

A Personal Library for Fast Data Analytics

python

pythonrobotics

Python sample codes for robotics algorithms.

rob-545-final-project

Trajectory Optimization with Drake

robotic

This project develops a sample-based motion-planning algorithm for robot with differential constraints.

robotics-path-planning-04-quintic-polynomial-solver

Udacity Self-Driving Car Engineer Nanodegree: Quintic Polynomial Solver. & Paper 'Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame'

self-driving-car-sim

A self-driving car simulator built with Unity

self-driving-cars

self_driving_car_specialization

Assignments and notes for the Self Driving Cars course offered by University of Toronto on Coursera

self_driving_cars_specialization

Exercises from the Self-Driving Cars Specialization by the University of Toronto on Coursera

selfdrivingcarscontroldesign

Self Driving Cars Longitudinal and Lateral Control Design

shortest-paths-in-graphs-of-convex-sets

social-lstm

Social LSTM implementation in PyTorch

social-lstm-tf

Tensorflow implementation of the Social LSTM model

social_lstm_pedestrian_prediction

The aim of the project is to predict the trajectories of pedestrians using lstm neural networks. The project starts from the paper "Social LSTM: Human Trajectory Prediction in Crowded Spaces - Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese - Stanford University - The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 961-971", and its official implementation (https://github.com/vvanirudh/social-lstm-tf) and makes some modifications. This is the Multimedia communication course project made during my final year of the bachelor degree under the supervision of professor Nicola Conci and his phd student Niccolò Bisagno. The modifications introduced are two: - To every simulated pedestrian add the input goal; the goal is the final position (in x and y coordinates) of that pedestrian when it disappears from the video. This modification should improve the predicted trajectory of that pedestrian because of the introduction of this new information - The grid created for every pedestrian in the original project to identify nearby pedestrians is replaced with an array containing the position(in x and y coordinates) of the others pedestrians in distance order, from the closest to the farther. This modification should improve the model results beacuse it presents relevant informations in order to the neural network. Then these two modifications were combined in to a single model. Every model has been evaluated in the test videos with different parameters and in conclusion the model with the two modifications (goal and array) combined performed better than any other model. Also the two modify models performed better than the original model. These results can be seen in the report at page 12 and 13. Unfortunately I haven't the time to translate the report in english, because now is in italian, but the result table at page 12 an 13 should be pretty clear. Technical details: - Programming language: Python 2.7 - Neural networks library used: Tensorflow 1.5 - External libraries: CUDA 8.0, CUDNN 6.0 - OS: Linux, Ubuntu 16.04 distrubution License: GPL v3

ss-lstm

SS-LSTM model for pedestrian trajectory prediction

tensorflow

Computation using data flow graphs for scalable machine learning

thebeatles

All albums of The Beatles band

topologicalmap

topological map for slam

underactuated

The course text for MIT 6.832 (and 6.832x on edX)