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 151–160 of 324 papers

TitleStatusHype
Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind—0
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing—0
Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework—0
Llamarine: Open-source Maritime Industry-specific Large Language Model—0
Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning—0
Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles—0
Long-Term Planning Around Humans in Domestic Environments with 3D Scene Graphs—0
LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments—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