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.

Tracking the position of moving objects requires tight coordination of sensing and movement, in both biological contexts such as prey pursuit and capture, and in target localization by mobile robots. Algorithms for target tracking often use a probabilistic map, or information map, of the domain to guide active search. Though it is reasonable to expect that the best approach would be to choose control actions driving the robot toward the maximum of this information map, we show improved performance in simulation by using a simple heuristic incorporating the time history of robot movement into the map. Furthermore, our results indicate that as the distribution of robot positions approaches the distribution of the density of information, the variance of the estimate is decreased and tracking improves. We conclude that control actions based solely on information maximization may under-perform in information orientated tasks, such as the estimation of moving target positions.

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.

An optimization-based estimation method is presented to determine mode transition times and model parameters for hybrid systems. First- and second-order optimality conditions are derived, including cross-derivative terms between transition times and parameters. Second-order optimization methods are shown to provide superior convergence to correct values in simulation, and to values within expected ranges experimentally for traction estimation of a skid-steered vehicle.

This paper presents a method of simultaneous mode transition time and parameter estimation for hybrid systems based on switching time optimization techniques. A concise derivation of first- and second-order optimality conditions with respect to both mode transition times and parameter values is presented, including cross-derivative terms between the switching times and the parameters. The estimation algorithm is shown to be effective for estimating transition times as well as unknown parameter values from coarsely sampled data for a skid-steered vehicle, which traverses unknown or changing terrain and transitions between discrete dynamic modes. It is shown that second-order optimization methods using the exact Hessian provide far superior convergence, compared to first-or approximate second-order methods, to correct values in simulated and experimental scenarios.