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 61–70 of 324 papers

TitleStatusHype
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
Computing Forward Reachable Sets for Nonlinear Adaptive Multirotor Controllers—0
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning—0
BoT-Drive: Hierarchical Behavior and Trajectory Planning for Autonomous Driving using POMDPs—0
A Practical and Online Trajectory Planner for Autonomous Ships' Berthing, Incorporating Speed Control—0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning—0
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios—0
A nonlinear tracking model predictive control scheme for dynamic target signals—0
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks—0
Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning—0
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All datasetsnuScenesToolBench

Benchmark Results

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