This paper presents an active search trajectory synthesis technique for autonomous mobile robots with nonlinear measurements and dynamics. The presented approach uses the ergodicity of a planned trajectory with respect to an expected information density map to close the loop during search. The ergodic control algorithm does not rely on discretization of the search or action spaces and is well posed for coverage with respect to the expected information density whether the information is diffuse or localized, thus trading off between exploration and exploitation in a single-objective function. As a demonstration, we use a robotic electrolocation platform to estimate location and size parameters describing static targets in an underwater environment. Our results demonstrate that the ergodic exploration of distributed information algorithm outperforms commonly used information-oriented controllers, particularly when distractions are present.

This paper presents an algorithm for active search where the goal is to calculate optimal trajectories for autonomous robots during data acquisition tasks. Formulating the problem as parameter estimation enables us to use Fisher information to create an explicit connection between robot dynamics and the informative regions of the search space. We use optimal control to automate design of trajectories that spend time in regions proportional to the probability of collecting informative data and use acquired data to update the probability closed-loop. Experimental and simulated results use a robotic electrosense platform to localize a feature in one-dimension. We demonstrate that this method is robust with respect to disturbances and initial conditions, and results in successful localization of the feature with a 100% experimental success rate and a 34% reduction in localization time compared to the next best tested controller.

Certain electric fish use a self-generated AC electric field to navigate and hunt. Thousands of sensors on the surface of the fish's body detect the pattern of amplitude and phase distortions of the field caused by nearby objects. Prior research has suggested that phase distortions may be especially useful for recognition of live objects. Here we present the first study of the utility of phase information in a robotic implementation of active electrosense. Using our robotic implementation, we investigated how the phase information depends on the frequency of the emitted field, the conductivity of the surrounding water, and object properties. An analytical model was developed serving as qualitative explanation of the dependency. We show that in certain situations phase information enables discrimination between two objects that are otherwise very similar in the amplitude of their electric images. We also show the utility of probing objects with multiple frequencies.

We have designed an underwater robot that uses perturbations of an emitted electric field to sense, localize, and map its environment. This system is inspired by weakly electric fish, which emit an electric field to sense objects, localize prey, and communicate. When nearby objects distort the electric field, electroreceptors (fish) or voltage sensors (robot) detect these perturbations. Further analysis of the perturbations can reveal information about the associated target, such as size, shape, and distance. One difficulty with extracting distance-to-target for our robotic electrosense platform is that the measurements are dependent on orientation of the robot with respect to the object. We solve this problem by applying techniques from range-only SLAM, with modifications for some of the ways in which electrosense differs from the sensors typically used. We use two different Bayesian filters to estimate the orientation and position separately. Using this approach, we show that our electrosense robot can accurately localize and orient itself, and improve its estimate of position and orientation using motion.

Few effective technologies exist for sensing in dark or murky underwater situations. For this reason, we have been exploring the use of a novel biologically-inspired approach to non-visual sensing based on the detection of perturbations to a self generated electric field. This is used by many species of neotropical nocturnal freshwater fish. This approach, termed active electrosense, presents unique challenges for sensing and tracking of nearby objects. We explore two methods for estimating the velocity of objects through active electrosense. The first of these methods uses a simple cross-correlation method, which depends on the uniformity of the electric field. We show some of the ramifications of making this assumption for a self-generated field around a cylindrical pod-shaped sensor in a rectangular tank. We then evaluate the use of methods developed for electrical impedance tomography (EIT) for localization and tracking. This is an unusual application of EIT in that typical applications involve surrounding the volume of interest (such as the thorax of humans) with sensor/emitters. Here, rather than this “outside in” approach, we are using EIT “inside out.” In simulation, we nonetheless find significant improvements in the accuracy of estimated velocity when using the EIT approach. Additionally, we demonstrate how EIT may be used for accurate position estimation. Under the conditions evaluated, the computation time for inversion is low enough to make its use feasible as a primary position and velocity estimator in an on- line system or as a secondary system to augment a computationally inexpensive estimator.