位置不確実性下での3D衝突回避を伴うMAVナビゲーションのための統合NMPCスキーム
A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty
3D点群から制約平面を抽出してNMPCの非線形制約に組み込み、シャノンエントロピーで位置推定の不確実性を考慮することで、位置ドリフトがあっても衝突回避を保証するMAV自律航法フレームワークを提案した論文。
著者: Sina Sharif Mansouri, Christoforos Kanellakis, Bjorn Lindqvist, Farhad Pourkamali-Anaraki, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs and guarantees real-time performance. Our first contribution is to design a computationally efficient subspace clustering method to reveal from geometrical constraints to underlying constraint planes within a 3D point cloud, obtained from a 3D lidar scanner. The second contribution of our work is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization and NMPC using the Shannon entropy. This step enables us to track either the position or velocity references, or none of them if necessary. As a result, the collision avoidance constraints are defined in the local coordinates of MAVs and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. Additionally, as the platform continues the mission, this will result in less uncertain position estimations, due to the feature extraction and loop closure. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.
