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
A Simple and Model-Free Path Filtering Algorithm for Smoothing and Accuracy
In this paper, we present a model-free path filtering method based on the popular moving average method, namely the Curvature Corrected Moving Average (CCMA), which convinces by its simplicity and broad applicability.
RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
We propose a novel approach to multi-robot collaboration that harnesses the power of pre-trained large language models (LLMs) for both high-level communication and low-level path planning.
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
cirrick/multi_uav_data_harvesting
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Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms.
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles
When used by autonomous vehicles for trajectory planning or obstacle avoidance, depth estimation methods need to be reliable.
Convex Risk Bounded Continuous-Time Trajectory Planning and Tube Design in Uncertain Nonconvex Environments
To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
E2E-AD/AD-MLP
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Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm.
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left).
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles.
