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Collision Avoidance

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/95616ARG/PRDNN

공식 코드

Provable Repair of Deep Neural Networks

https://github.com/95616ARG/SyReNN

공식 코드

A Symbolic Neural Network Representation and its Application to Understanding, Verifying, and Patching Networks

https://github.com/AUTOBarge/autobargesim

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AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels

https://github.com/AdaCompNUS/GAMMA

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GAMMA: A General Agent Motion Model for Autonomous Driving

https://github.com/Airspace-Encounter-Models/em-model-manned-bayes

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Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes

https://github.com/CognitiveAISystems/Dynamic-Neural-Potential-Field

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Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles

https://github.com/Colin97/Point2Mesh

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Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance

https://github.com/DataStories-UniPi/FLP-XR

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FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time

https://github.com/JingyuanZhou/Safe_RL_Mixed_Traffic

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Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning

https://github.com/KTH-RPL-Planiacs/sample_based_bcbf

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Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions

https://github.com/KafuuChikai/Dashing-for-the-Golden-Snitch-Multi-Drone-RL

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Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning

https://github.com/MIT-REALM/gcbf-pytorch

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Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control

https://github.com/MPC-Berkeley/hmpc_raidnet

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Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions

https://github.com/MartinBraquet/vector-field-obstacle-avoidance

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Vector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape

https://github.com/NVlabs/CTG

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Guided Conditional Diffusion for Controllable Traffic Simulation

https://github.com/PathPlanning/ORCA-algorithm

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A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

https://github.com/SDoodeman/bpe_quadrotor

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Leader-Follower Formation Tracking Control of Quadrotor UAVs Using Bearing Measurements

https://github.com/StanfordASL/LearnedSamplingDistributions

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Learning Sampling Distributions for Robot Motion Planning

https://github.com/XuankangLin/ART

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ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

https://github.com/YONEX4090/MultiQuadruped-Control

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Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots

https://github.com/abhi-iyer/meta-learning

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Collision Avoidance Robotics Via Meta-Learning (CARML)

https://github.com/accgen99/marl_ipp

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Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning

https://github.com/agrimgupta92/sgan

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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/alexfrom0815/Online-3D-BPP-DRL

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Online 3D Bin Packing with Constrained Deep Reinforcement Learning

https://github.com/allenai/disturb-free

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Towards Disturbance-Free Visual Mobile Manipulation

https://github.com/anshantju/hdes-net

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Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems

https://github.com/bassamlab/sigmarl

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High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation

https://github.com/caipeide/VTGNet

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VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

https://github.com/ch9397/fixedwing-monoppo

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Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs

https://github.com/connectedsystemslab/batmobility_ae

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BatMobility: Towards Flying Without Seeing for Autonomous Drones

https://github.com/ctu-mrs/flight_forge

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FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy

https://github.com/cvml-cfu/ecam

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ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting

https://github.com/data-science-in-mechanical-engineering/et-distributed-uav-path-planner

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Event-triggered and distributed model predictive control for guaranteed collision avoidance in UAV swarms

https://github.com/duanyuan13/pcm

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Revisiting Stereo Triangulation in UAV Distance Estimation

https://github.com/emunaran/stochastic-human-driving-policies-drl

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Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

https://github.com/ersindas/Zero-order-CBFs

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Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.