On active target tracking and cooperative localization for multiple aerial vehicles

Abstract

Abstract-This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target's position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target's position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors' accuracy and target's dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multitarget Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets.

Active Perception Based Formation Control for Multiple Aerial Vehicles

“…Multi-robot active tracking: In [6], multi-robot trajectories are analytically derived and uncertainty bounds are identified for optimizing the fused estimate of the target position. In [7], a non-convex optimization problem is solved analytically, to minimize the trace of the fused target covariance.…”

Hierarchical control of a team of quadrotors for cooperative active target tracking

“…This paper takes its inspiration from [7], [8] and makes several novel contributions. As it is evident from the previous literature review, a large body of research exists on cooperative active target tracking for wheeled robots,butfew works in the literature (cf., [12], [13]) have dealt with UAVs. In addition, oversimplified models (e.g., firstor secondorder integrators) are typically used for the aerial vehicles.…”

“…the angular velocity for the four propellers). Differently from [13], a discrete-time Kalman filter is used here for fusing the local estimates provided by each aerial vehicle. Under suitable conditions, it is shown that the cost function for the Doptimality criterion that the quadrotors aim at collaboratively reduce, has a single global minimum and no local minima.…”

“…The measurement model of each quadrotor is illustrated in Fig. 3 (cf., [13]). A target frame, denoted by {T }, is attached to the target and aligned with the world reference frame, {W}, at all times.…”

Decentralized cooperation of multiple UAS for multi-target surveillance under uncertainties

“…Several indexes can be used to measure the amount of information, like entropy, mutual information, etc. Most of the works assume Gaussian uncertainties and Kalman (or Information) Filters as the underlying estimation framework, and define utility functions based on these information measures to determine the actuations [12], [13], [14]. However, tracking applications can result in multi-modal distributions when estimating the targets' positions.…”