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 91–100 of 324 papers

TitleStatusHype
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks—0
Emotion Based Prediction in the Context of Optimized Trajectory Planning for Immersive Learning—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning—0
A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning—0
Aether: Geometric-Aware Unified World Modeling—0
Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling—0
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles—0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning—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