Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet

TL;DR

This work proposes an online task and motion coordination algorithm that ensures an explicitly-bounded estimation uncertainty for the target states, while minimizing the average number of active robots.

Abstract

Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discrete actions while neglecting the dynamic constraints of the robots and unknown behaviors of the targets. This work proposes an online task and motion coordination algorithm that ensures an explicitly-bounded estimation uncertainty for the target states, while minimizing the average number of active robots. The robots have a limited-range perception to actively track a limited number of targets simultaneously, of which their future control decisions are all unknown. It includes: (i) the assignment of monitoring tasks, modeled as a flexible size multiple vehicle routing problem with time windows (m-MVRPTW), given the predicted target trajectories with uncertainty measure in the road-networks; (ii) the nonlinear model predictive control (NMPC) for optimizing the robot trajectories under uncertainty and safety constraints. It is shown that the robots can switch between active and inactive roles dynamically online as required by the unknown monitoring task. The proposed methods are validated via large-scale simulations of up to $100$ robots and targets.

Figures (5)

• Figure 1: Illustration of the considered scenario. Left: $3$ UAVs (in blue) are actively monitoring $10$ targets (in red) within a road network, with $7$ inactive ones (in orchid) that are recruited later online; Top-right: Evolution of estimation uncertainty for all targets below a specified upper-bound (in black); Bottom-right: Number of active UAVs in the fleet and number of targets assigned to each active UAV.
• Figure 2: Illustration of how the posteriori uncertainty $\widetilde{\Sigma}_m$ changes by \ref{['eq:total-post']}, compared with the prior $\widehat{\Sigma}_m$: on one road (Left) and an interaction (Right).
• Figure 3: Illustration of the assignment algorithm given the predicted trajectories with covariances, i.e., $5$ targets monitored by $2$ robots.
• Figure 4: Left: Snapshot at $t=52s$ where $4$ robots are active. Right: Final trajectories of all robots with marked status.
• Figure 5: Comparison against four baselines: the maximum uncertainty of all targets (Left) and the number of active robots within the fleet (Right).

Theorems & Definitions (4)

• Remark 1
• Remark 2
• Remark 3
• Remark 4