Active Information Acquisition under Arbitrary Unknown Disturbances
This work addresses a critical limitation in active information acquisition for robotics, enabling more robust tracking in real-world scenarios with unpredictable disturbances, though it is incremental as it builds on existing optimization frameworks.
The paper tackles the problem of planning sensor trajectories for robots to actively gather information about targets subject to arbitrary unknown disturbances, where traditional methods rely on known statistical models. It proposes a suboptimal solution using Forward Value Iteration with pruning, establishing performance guarantees for tracking both state and unknown inputs, and demonstrates in simulations that it outperforms a greedy policy.
Trajectory optimization of sensing robots to actively gather information of targets has received much attention in the past. It is well-known that under the assumption of linear Gaussian target dynamics and sensor models the stochastic Active Information Acquisition problem is equivalent to a deterministic optimal control problem. However, the above-mentioned assumptions regarding the target dynamic model are limiting. In real-world scenarios, the target may be subject to disturbances whose models or statistical properties are hard or impossible to obtain. Typical scenarios include abrupt maneuvers, jumping disturbances due to interactions with the environment, anomalous misbehaviors due to system faults/attacks, etc. Motivated by the above considerations, in this paper we consider targets whose dynamic models are subject to arbitrary unknown inputs whose models or statistical properties are not assumed to be available. In particular, with the aid of an unknown input decoupled filter, we formulate the sensor trajectory planning problem to track evolution of the target state and analyse the resulting performance for both the state and unknown input evolution tracking. Inspired by concepts of Reduced Value Iteration, a suboptimal solution that expands a search tree via Forward Value Iteration with informativeness-based pruning is proposed. Concrete suboptimality performance guarantees for tracking both the state and the unknown input are established. Numerical simulations of a target tracking example are presented to compare the proposed solution with a greedy policy.