SOTAVerified

Trajectory Planning

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.

Papers

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Showing 51–75 of 324 papers

TitleStatusHype
SwissNYF: Tool Grounded LLM Agents for Black Box SettingCode0
Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic ParticipantsCode0
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool UseCode0
Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic UncertaintyCode0
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language ModelsCode0
Parallelization of Monte Carlo Tree Search in Continuous DomainsCode0
Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile RobotsCode0
Non-iterative Optimization of Trajectory and Radio Resource for Aerial NetworkCode0
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic ReasoningCode0
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree SearchCode0
MMP++: Motion Manifold Primitives with Parametric Curve ModelsCode0
Deep Movement Primitives: toward Breast Cancer Examination RobotCode0
Data Assimilation in Chaotic Systems Using Deep Reinforcement LearningCode0
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict ResolutionCode0
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous DrivingCode0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein PolynomialCode0
Machine Learning Optimized Orthogonal Basis Piecewise Polynomial ApproximationCode0
Computing Forward Reachable Sets for Nonlinear Adaptive Multirotor Controllers—0
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks—0
Combining Belief Function Theory and Stochastic Model Predictive Control for Multi-Modal Uncertainty in Autonomous Driving—0
Articulatory modeling of the S-shaped F2 trajectories observed in Öhman's spectrographic analysis of VCV syllables—0
A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks—0
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All datasetsnuScenesToolBench

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-P3 (Lidar)Collision-3s1.27—Unverified
2UniADCollision-3s0.71—Unverified
3AD-MLPCollision-3s0.24—Unverified
4VAD-Base [jiang2023vad]Collision-3s0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4-TOPGUNWin rate86.54—Unverified
2Attention BucketWin rate71.5—Unverified
3GPT4- DFSDTWin rate70.4—Unverified