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Trajectory Planning
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Trajectory planning for industrial robots consists of moving the tool center point from point A to point B while avoiding body collisions over time. Trajectory planning is sometimes referred to as motion planning and erroneously as path planning. Trajectory planning is distinct from path planning in that it is parametrized by time. Essentially trajectory planning encompasses path planning in addition to planning how to move based on velocity, time, and kinematics.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Epona: Autoregressive Diffusion World Model for Autonomous Driving
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)
Interpreting learned search: finding a transition model and value function in an RNN that plays Sokoban
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness
Ego-centric Learning of Communicative World Models for Autonomous Driving
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning
From Turbulence to Tranquility: AI-Driven Low-Altitude Network
World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving
TrackVLA: Embodied Visual Tracking in the Wild
Attention-Enhanced Prompt Decision Transformers for UAV-Assisted Communications with AoI
Articulatory modeling of the S-shaped F2 trajectories observed in Öhman's spectrographic analysis of VCV syllables
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving
VERDI: VLM-Embedded Reasoning for Autonomous Driving
Provably safe and human-like car-following behaviors: Part 1. Analysis of phases and dynamics in standard models
Provably safe and human-like car-following behaviors: Part 2. A parsimonious multi-phase model with projected braking
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving
Optimal Trajectory Planning with Collision Avoidance for Autonomous Vehicle Maneuvering
Trajectory Planning with Model Predictive Control for Obstacle Avoidance Considering Prediction Uncertainty
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior
Adaptive Field Effect Planner for Safe Interactive Autonomous Driving on Curved Roads
Generative emulation of chaotic dynamics with coherent prior
Diffusion Models for Robotic Manipulation: A Survey
CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving
Safe and Efficient Coexistence of Autonomous Vehicles with Human-Driven Traffic at Signalized Intersections
Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models
Interpreting Emergent Planning in Model-Free Reinforcement Learning
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.