An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

Abstract

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction onboard a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.

Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

“…In the year of 2020, L. Schmid et al. [ 27 ] presented an RRT*-inspired online informative path planning algorithm. This algorithm could achieve global coverage and maximize the utility of a path in a global context, using a single objective function.…”

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

“…These methods have been mostly applied for object reconstruction with robot manipulators, under strong assumptions on the navigable free space. However, several works have extended NBV to mobile robots by considering a volumetric representation [10], [12] and more recently the TSDF [1], [13], [14]. On the other hand, volumetric exploration aims at exploring a predefined volume containing the object of interest.…”

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

“…Various algorithms have been developed for path planning according to the application scenarios [2], e.g. RRT methods for structured [3] and unstructured driving scenarios [4], A * algorithm for off-line path planning [5] [6], polynomial interpolation approaches for real-time motion planning [7], etc. Meanwhile, the driving safety is essential for autonomous driving which should be ensured and improved [8].…”