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 226–250 of 324 papers

TitleStatusHype
Mars Entry Trajectory Planning with Range Discretization and Successive Convexification—0
Safe-by-Design Planner-Tracker Synthesis—0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model—0
Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points—0
MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error PropagationCode1
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstaclesCode1
Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments—0
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
Real-World Application of Various Trajectory Planning Algorithms on MIT RACECAR—0
Trajectory Planning Under Environmental Uncertainty With Finite-Sample Safety GuaranteesCode0
UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning—0
Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target—0
Multistage Stochastic Model Predictive Control for Urban Automated Driving—0
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs—0
OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots—0
Stability Constrained Mobile Manipulation Planning on Rough Terrain—0
Fast Spline Trajectory Planning: Minimum Snap and Beyond—0
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Shared-phase-dedicated-lane based intersection control with mixed traffic of human-driven vehicles and connected and automated vehicles—0
RIS-Assisted UAV for Timely Data Collection in IoT Networks—0
IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways—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