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 31–40 of 324 papers

TitleStatusHype
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications—0
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language ModelsCode0
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action ModelCode4
Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration—0
Aether: Geometric-Aware Unified World Modeling—0
Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR—0
Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles—0
Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets—0
A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning—0
Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban 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