センシングロボットによる情報獲得：アルゴリズムと誤差限界
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
移動センサの軌道最適化において、センサ動特性を考慮しつつ劣モジュラ性に依存しない非貪欲アルゴリズムを提案し、性能保証付きで貪欲法より優れることを示した。
著者: Nikolay Atanasov, Jerome Le Ny, Kostas Daniilidis, George J. Pappas
分類: eess.SY, cs.RO, cs.SY, math.DS, math.OC
原文アブストラクト
Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.
