Results show that the developed HA algorithms are effective at detecting hazards and guiding the lander to a safe LS.
Cross-hole seismic surveys are well established for non-invasive investigations of geological structure and are well suited for detecting hazards beneath buildings.
In order to perform a safe landing, the vehicle should be capable of detecting hazards, estimating its fuel consumption as well as touchdown performance, and locating a safe spot to land.
During HA, sensors and computers onboard the lander are used to detect hazards in the landing zone, autonomously select the most suitable region for landing, and generate the trajectory that retargets the lander to the safer landing site (LS).
Since 1980s, interferometry synthetic aperture radar (InSAR) technique has been utilized to monitor the deformation of the Earth's surface [1] and applied to detecting hazards, e.g., earthquakes [2], ice motion [3], volcanism [4], landslides [5], and groundwater flow [6].
In so doing, the research added an environmental safeguard dimension which is critical in detecting hazards and ensuring the welfare of communities and the preservation of a delicate ecosystem already overstretched by stressors.
Misstep, loss of footing, and step on/in related incidents were more common during egress and are likely due to the operator's reduced visibility when descending a ladder facing the equipment, limiting their ability to detect hazards.
As a passive system with a natural adaptation capacity to the environment, mimicking the human capacity for detecting hazards, vision-navigation has been the subject of a sustained research effort for more than ten years.
First, image positions of detected hazards are used to generate affine invariants, after that, affine invariants are exploited to generate representative sets to describe hazard, thirdly, two finite set based hazard matching algorithms that based upon seeking similarity of representative sets are proposed, in which the advantage of set based feature matching technique is highlighted.
As a passive light-weight system with a natural adaptation capacity to the environment, mimicking the human capacity for detecting hazards, vision-based navigation for space applications has been the subject of a sustained research effort in Europe for more than ten years.
