Information Acquisition with Sensing Robots: Algorithms and Error Bounds
This addresses the challenge of efficient information acquisition for mobile sensing robots, offering improved performance guarantees over existing greedy approaches, though it is incremental in advancing trajectory optimization methods.
The paper tackles the problem of optimizing trajectories for mobile sensors to gather information, presenting a non-greedy algorithm with suboptimality guarantees that accounts for sensor dynamics and outperforms greedy methods, with applications in gas concentration mapping and target tracking.
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.