This paper describes an onboard automated hybrid reasoning system for terrain safety assessment using multiple heterogeneous sensors onboard a spacecraft. An innovative feature of this system is integration of multiple sensing modalities and different reasoning engines into a unified approach. Three different frameworks for representation of data uncertainty are considered: fuzzy set theory, Bayesian probability theory, and Dempster-Shafer belief theory. The hybrid reasoning system is composed of three subsystems, namely, multisensor fusion, multidecision fusion, and tier-based fusion selection, where each tier represents a range of spacecraft altitudes. Simulation results are presented to illustrate the multisensor decision fusion methodology described in this paper. The developed capabilities will enable future spacecraft to land safely in more challenging planetary regions with scientifically interesting terrain features.

In this paper, multiple on-board sensors are used to assess the terrain safety in real-time during spacecraft descent. A linguistic, fuzzy rule-based reasoning engine is used to determine terrain safety from sensor measurements and, together with information about required fuel consumption and site science return, provide a figure-of-merit for all possible landing sites. Additional fuzzy rule-sets are used to address spatial and temporal dependence in the reasoning process in order to arrive at a final score for each potential landing site. This landing score is used to retarget the spacecraft if the original landing site is found to be hazardous. Simulation studies are presented for illustration.

The success of a landed space exploration mission depends largely on the final landing site. Factors influencing site selection include safety, fuel-consumption, and scientific return. This paper addresses the problem of selecting the best available landing site based on these factors in real-time during autonomous spacecraft descent onto a planetary surface. The problem is modeled probabilistically using Bayesian networks (BNs). BNs provide a means of representing the causal relationships between variables that impact the quality of a landing site. The final landing site is determined via probabilistic reasoning based on terrain safety derived from on-board sensors, available fuel based on spacecraft descent dynamics, and regions of interest defined by mission scientists