Computationally Efficient Information-Theoretic Exploration of Pits and Caves


This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks to achieve real-time operation on computationally constrained systems while ensuring energy-efficient information acquisition. Trajectories are selected by maximizing a measure of information gain per an expected execution cost (e.g., time or energy). The proposed trajectory generation formulation is based on state-lattice motion primitives and evaluation of the Cauchy-Schwarz quadratic mutual information (CSQMI) at each lattice state. An expanded search structure is proposed that extends the state-lattice to a finite horizon to enable expansive space coverage while remaining real-time viable. Additionally, compression techniques are employed to reduce the computational burden associated with the CSQMI calculation over expansive environments while preserving fidelity. The performance of the proposed methodology is evaluated through simulated exploration of a three-dimensional terrestrial pit environment by a quadrotor aerial robot which acts as a surrogate for a propulsive vehicle when operating on an airless body.



Computationally efficient information-theoretic exploration of pits and caves W Tabib, M Corah, N Michael, R Whittaker Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on