FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

2021 ◽  
pp. 1-12
Author(s):  
Á. Martínez Novo ◽  
Liang Lu ◽  
Pascual Campoy

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.

Author(s):  
E. Palazzolo ◽  
C. Stachniss

Most micro aerial vehicles (MAV) are flown manually by a pilot. When it comes to autonomous exploration for MAVs equipped with cameras, we need a good exploration strategy for covering an unknown 3D environment in order to build an accurate map of the scene. In particular, the robot must select appropriate viewpoints to acquire informative measurements. In this paper, we present an approach that computes in real-time a smooth flight path with the exploration of a 3D environment using a vision-based MAV. We assume to know a bounding box of the object or building to explore and our approach iteratively computes the next best viewpoints using a utility function that considers the expected information gain of new measurements, the distance between viewpoints, and the smoothness of the flight trajectories. In addition, the algorithm takes into account the elapsed time of the exploration run to safely land the MAV at its starting point after a user specified time. We implemented our algorithm and our experiments suggest that it allows for a precise reconstruction of the 3D environment while guiding the robot smoothly through the scene.


2016 ◽  
Vol 1 (1) ◽  
pp. 153-160 ◽  
Author(s):  
Philip M. Dames ◽  
Mac Schwager ◽  
Daniela Rus ◽  
Vijay Kumar

2021 ◽  
pp. 109767
Author(s):  
Ran Xiao ◽  
Xiang Li ◽  
Huaiyuan Jia ◽  
James Utama Surjadi ◽  
Jingqi Li ◽  
...  

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