A robot(boat) that autonomously navigate with the optimal path and obstacle avoidance through deep reinforcement learning is being produced.
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Updated
Nov 21, 2022 - Python
A robot(boat) that autonomously navigate with the optimal path and obstacle avoidance through deep reinforcement learning is being produced.
Code base for SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization
Aircraft detection and collision avoidance
Assignment 3 in course DD2438 Artificial Intelligence and Multi Agent Systems at KTH Royal Institute of Technology.
Python wrapper for the Powerlaw Collision Avoidance model
Updated version can be found at https://github.com/MIT-REALM/density_planner
Questa repository rappresenta il risultato del mio lavoro di tesi, focalizzato sullo sviluppo di un Laser-Based Collision Avoidance System per applicazioni robotiche. Il sistema è stato progettato per migliorare la sicurezza dei robot e prevenire le collisioni utilizzando la tecnologia basata su laser.
Deep Predictive models for collision risk assessment in autonomous driving
This repository extracts 3D-coordinates of joint positions of a humanoid using OpenPose and a IntelRealSense Depth-Camera. With those joints it simulates a humanoid having spheres and cylinders as limbs in PyBullet. It is designed detect humans for collision avoidance for robots (proof of concept).
Code used in my Graduation Project as requirement for obtaining the degree of Information Technology Engineer.
A small game with a cube as your main hero and try to avoid your enemy colliders.
Random generation of circles bounded by an anti-collision system
This repository contain code for Collision avoidance, Monocular and Stereo Depth Estimation
Repo for Collaboration between Watchtower Foundation and Metasolis Labs on the Token Engineering for the Watchtower platform
An environment for urban area autonomous driving and pedestrian collision avoidance.
Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).
Design and integration of Guidance, Navigation and Control systems for autonomous robot swarms
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