Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

by   Kalyan Vasudev Alwala, et al.

Among the most prevailing motion planning techniques, sampling and trajectory optimization have emerged successful due to their ability to handle tight constraints and high-dimensional systems respectively. However, limitations in sampling in higher dimensions and local minima issues in optimization have hindered their ability to excel beyond static scenes in offline settings. Here we consider highly dynamic environments with long horizons that necessitate a fast online solution. We present a unified approach that leverages the complementary strengths of sampling and optimization, and interleaves them both in a manner that is well suited to this challenging problem. With benchmarks in multiple synthetic and realistic simulated environments, we show our approach is significantly better in performance on various metrics against baselines that only either employ sampling or optimization. Supplementary video:


page 1

page 5

page 6


Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree

We present an algorithm to visualize local minima in a motion planning p...

Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes

Trajectory optimization methods for motion planning attempt to generate ...

Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots

Planning for legged-wheeled machines is typically done using trajectory ...

Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight

For real-time multirotor kinodynamic motion planning, the efficiency of ...

Dispertio: Optimal Sampling for Safe Deterministic Sampling-Based Motion Planning

A key challenge in robotics is the efficient generation of optimal robot...

Parallelised Diffeomorphic Sampling-based Motion Planning

We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PD...

Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles

We propose a novel method for planning shortest length piecewise-linear ...