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 271–280 of 324 papers

TitleStatusHype
AlphaPilot: Autonomous Drone Racing—0
Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind—0
A message-passing algorithm for multi-agent trajectory planning—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition—0
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning—0
A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning—0
A nonlinear tracking model predictive control scheme for dynamic target signals—0
A Practical and Online Trajectory Planner for Autonomous Ships' Berthing, Incorporating Speed Control—0
A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization—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