Autonomous systems comprised of many cooperative agents have the potential for enabling long-duration tasks and data collection critical to the understanding of a wide range of phenomena in spatially and temporally variable environments. The adaptive distributed optimal control approach presented in this article extends online approximate dynamic programming to very-large-scale robotics (VLSR) systems that must operate and adapt to highly uncertain and variable environments. Optimal mass transport theory is used to show that, in the Wasserstein-Gaussian mixture model space, the VLSR system's cost to go can be represented by a value functional of the robot distribution and dynamic environmental maps. The approach is demonstrated on a cooperative path planning problem in which knowledge of the obstacles in the environment changes incrementally over time based on in situ measurements. Numerical simulations show that the proposed approach significantly outperforms existing methods by finding an approximately optimal solution that avoids obstacles and meets a desired final robot distribution using minimum energy.

This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.

Information value functions based on the Kullback- Leibler divergence have been shown the most effective for planning sensor measurements using greedy strategies. However, this paper shows that the problem of optimally controlling a sensor over a finite time horizon is NP-hard. The new lexicographic approach presented in this paper relies on new information value functions that are shown to be additive and can be optimized efficiently over time. Also, this paper derives a convex approximation of the sensor nonlinear field-of-view that allows for real-time sensor control, and is accompanied by performance guarantees. Extensive results on real pedestrian data show that the lexicographic control algorithm significantly improves modeling and prediction performance when compared to existing algorithms.

Information-driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in uncertainty also known as information gain. Random finite set (RFS) theory provides a formalism for quantifying and estimating information gain in multi-object tracking problems. However, estimating information gain in these applications remains computationally challenging. This paper presents a new tractable approximation of the RFS expected information gain applicable to sensor control for multi-object search and tracking. Unlike existing RFS approaches, the information gain approximation presented in this paper considers the contributions of non-ideal noisy measurements, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information-driven sensor control is demonstrated through two multi-vehicle search-while-tracking experiments using real video data from remote terrestrial and satellite sensors.

This paper presents a visibility-based method for planning the motion of a mobile robotic sensor with bounded field-of-view to optimally track a moving target while localizing itself. The target and robot states are estimated from online sensor measurements and a set of a priori known landmarks, using an extended Kalman filter (EKF), and thus the proposed method is applicable to robots without a global positioning system. It is shown that the problem of optimizing the target tracking and robot localization performance is equivalent to optimizing the visibility or probability of detection in the EKF framework under mild assumptions. The control law that maximizes the probability of detection for a robotic sensor with a sector-shaped field-of-view (FoV) is derived as a function of the robot heading and aperture. Simulations have been conducted on synthetic experiments and the results show that the optimized-visibility approach is effective at avoiding target loss, and outperforms a state-of-the-art potential method based on robot trailer models [1].