Eclares: Energy-Aware Clarity-Driven Ergodic Search

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot's limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models, or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0,1]. The clarity dynamics allows us to capture information decay due to lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study.

Gatekeeper: Safety Critical Control of Nonlinear Systems with Limited Perception in Unknown and Dynamic Environments

This paper presents the Gatekeeper algorithm, a real-time method to guarantee the safety of a robotic system operating in environments that are unknown and dynamic. Given a nominal planner designed to meet mission objectives, Gatekeeper extends the nominal trajectories using backup controllers, and determines a control policy that is certified safe for all future time using the currently available information. We demonstrate the algorithm on a dynamic aerial firefighting mission, and show reduced conservatism relative to existing methods. The algorithm was also demonstrated onboard a quadrotor, where a map of the environment was built online, and the Gatekeeper algorithm prevented a human pilot from flying the quadrotor into obstacles and unknown regions.