Trajectory Planning
43 papers with code • 2 benchmarks • 8 datasets
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.
Datasets
nuScenes
highD Dataset
inD Dataset
ToolBench
rounD Dataset
exiD Dataset
uniD Dataset
short-MetaWorld
Latest papers with no code
Multi-Objective Trajectory Planning with Dual-Encoder
In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning.
Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving
However, no existing method combines motion prediction and trajectory planning in a joint step while guaranteeing equivariance under roto-translations of the input space.
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC
Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time.
Safe Planning through Incremental Decomposition of Signal Temporal Logic Specifications
Trajectory planning is a critical process that enables autonomous systems to safely navigate complex environments.
Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris
System performance is assessed in terms of capture success and overall fuel consumption by the MUs.
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Our contributions include (1) a reinforcement learning framework for UAV trajectory planning that dynamically integrates multi-objective considerations, (2) an analysis of human perceptions towards gendered and anthropomorphized drones in SAR contexts, and (3) the application of similarity-based experience replay for enhanced learning efficiency in complex SAR scenarios.
Automatic driving lane change safety prediction model based on LSTM
Autonomous driving technology can improve traffic safety and reduce traffic accidents.
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
To solve the problem, we introduce Branch and Play (B&P), an efficient and exact algorithm that provably converges to a socially optimal order of play and its Stackelberg equilibrium.
A Practical and Online Trajectory Planner for Autonomous Ships' Berthing, Incorporating Speed Control
Autonomous ships are essentially designed and equipped to perceive their internal and external environment and subsequently perform appropriate actions depending on the predetermined objective(s) without human intervention.
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins
Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks.
