Many scenarios require a robot to be able to explore its 3D environment online without human supervision. This is especially relevant for inspection tasks and search and rescue missions. To solve this high-dimensional path planning problem, sampling-based exploration algorithms have proven successful. However, these do not necessarily scale well to larger environments or spaces with narrow openings. This paper presents a 3D exploration planner based on the principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle (MAV)equipped with a limited field-of-view depth sensor randomly samples its configuration space to find promising future viewpoints. In order to obtain high sampling efficiency, our planner maintains and uses a history of visited places, and locally optimizes the robot's orientation with respect to unobserved space. We evaluate our method in several simulated scenarios, and compare it against a state-of-the-art exploration algorithm. The experiments show substantial improvements in exploration time (2 ⨯ faster), computation time, and path length, and advantages in handling difficult situations such as escaping dead-ends (up to 20 ⨯ faster). Finally, we validate the on-line capability of our algorithm on a computational constrained real world MAV.
@cite_3 uses NBVs in a 3D exploration algorithm. In a receding horizon fashion, their approach iterates between sampling accessible viewpoints in an RRT and executing the most informative path. Our algorithm builds up on theirs but introduces a memory of already visited spaces, local gain optimization, and trajectory optimization which leads to a significantly better sampling efficiency and shorter and faster explorations.
@cite_3
2409009991
This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.