**1.1. Background and context**

The first lunar exploration spacecraft named Luna 1 was flown to the moon on January 1959 [21]. Nonetheless, this mission did not give too much impact as it did not land on the moon itself. Due to the enthusiasm to continue the journey of previous research pioneers, Luna 2 became the first spacecraft to land on the moon's surface in late 1959 [21]. These histories of moon explorations became a motivation for a new researcher and moon explorer to find out more about Lunar and its unique features.

A crater plays a vital feature to estimate the age of the moon's surface when any sample specimen is not available [10, 11]. An autonomous crater detection algorithm will help space research scientists to reduce their laboratory works of manually identifying those craters. Previously, several automatic and semi-automatic crater detection algorithms were proposed [12], but their accuracy was not enough for craters chronology and they have yet to be fully tested for practical uses (example: spacecraft navigation). Craters chronology means the history or the sequence of events that formed the craters on the moon's surface and the variety of its features. Optical Landmark Navigation using craters on the planetary surface was first used operationally by the Near Earth Asteroid Rendezvous (NEAR) mission [15, 16]. This mission is to determine the spacecraft orbits and the range of the body for close flybys condition and low attitude orbiting [13].

Many planetary missions such as SELENE (Selenological and Engineering Explorer) and Clementine take the images of the moon's surface for on-going research. This attention to the moon exploratory especially will help us divulge the unimagined information and characteristics of planetary science specifically on the moon's surface. In 2006, a Japanese Lunar Orbiting Spacecraft was launched and was expected to bring a large amount of useful data for on-going planetary research. However, it is known that the images taken under the low sun elevation, such as those from 'Lunar Orbiter' and 'Apollo' are suitable for crater detection as mentioned before to differentiate the 'light and dark patches' for sooner analysis.

Current descent and landing technology for planetary operations, such as those of lunar, is performed by a landing error ellipse greater than 30x100 kilometres without terrain recognition or hazard avoidance capability. Most of the previous research on lunar pin point landing specifically has a limitation such that requires *a priori* reference map describing the past and future lunar imaging and digital elevation map data sets in order to detect the landmarks on a particular planetary surface. Due to this drawback, the authors propose a landmark-based detection algorithm named craters detection algorithm to detect main hazards on the moon's surface independently from those references in order to produce a reliable and repeated identification and detection system. This intelligent imagery-based algorithm will detect craters based on their pointing direction relative to the sun and classification to differentiate between the light and dark patches. Furthermore, by making a match of those detected craters with the internal lunar atlas, the Lander can further determine the spacecraft motion and velocity relative to the lunar surface.

### **1.2. State of the art**

484 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

The first lunar exploration spacecraft named Luna 1 was flown to the moon on January 1959 [21]. Nonetheless, this mission did not give too much impact as it did not land on the moon itself. Due to the enthusiasm to continue the journey of previous research pioneers, Luna 2 became the first spacecraft to land on the moon's surface in late 1959 [21]. These histories of moon explorations became a motivation for a new researcher and moon explorer to find out

A crater plays a vital feature to estimate the age of the moon's surface when any sample specimen is not available [10, 11]. An autonomous crater detection algorithm will help space research scientists to reduce their laboratory works of manually identifying those craters. Previously, several automatic and semi-automatic crater detection algorithms were proposed [12], but their accuracy was not enough for craters chronology and they have yet to be fully tested for practical uses (example: spacecraft navigation). Craters chronology means the history or the sequence of events that formed the craters on the moon's surface and the variety of its features. Optical Landmark Navigation using craters on the planetary surface was first used operationally by the Near Earth Asteroid Rendezvous (NEAR) mission [15, 16]. This mission is to determine the spacecraft orbits and the range of the body

Many planetary missions such as SELENE (Selenological and Engineering Explorer) and Clementine take the images of the moon's surface for on-going research. This attention to the moon exploratory especially will help us divulge the unimagined information and characteristics of planetary science specifically on the moon's surface. In 2006, a Japanese Lunar Orbiting Spacecraft was launched and was expected to bring a large amount of useful data for on-going planetary research. However, it is known that the images taken under the low sun elevation, such as those from 'Lunar Orbiter' and 'Apollo' are suitable for crater detection as mentioned before to differentiate the 'light and dark patches' for sooner

Current descent and landing technology for planetary operations, such as those of lunar, is performed by a landing error ellipse greater than 30x100 kilometres without terrain recognition or hazard avoidance capability. Most of the previous research on lunar pin point landing specifically has a limitation such that requires *a priori* reference map describing the past and future lunar imaging and digital elevation map data sets in order to detect the landmarks on a particular planetary surface. Due to this drawback, the authors propose a landmark-based detection algorithm named craters detection algorithm to detect main hazards on the moon's surface independently from those references in order to produce a reliable and repeated identification and detection system. This intelligent imagery-based algorithm will detect craters based on their pointing direction relative to the sun and classification to differentiate between the light and dark patches. Furthermore, by making a match of those detected craters with the internal lunar atlas, the Lander can further

determine the spacecraft motion and velocity relative to the lunar surface.

**1.1. Background and context** 

analysis.

more about Lunar and its unique features.

for close flybys condition and low attitude orbiting [13].

A spacecraft mission on the moon involving Entry, Descent and Landing (EDL) requires precise and intelligent landing techniques. There were numerous previous research efforts and various methods used to determine such landing sites that are safe for a moon Lander. Trying to get a new technique that can search for free hazards locations, this paper will propose an intelligent algorithm described as craters identification algorithm in order to recognize and detect craters consistently and repeatedly over most of the moon's surface. In addition, using geometric recognition techniques, the authors we can also determine the position, attitude, velocity and angular velocity of the spacecraft; the four important parameters used to land safely on the moon by finding a match of those detecting craters to a database containing the 3D locations of the craters (internal lunar atlas).

The lunar surface consists of several hazardous characteristics such as rocks, mountain, boulders, slopes and mainly craters. Particularly, in this paper, the authors choose craters as primary hazard detection because of its geometric shape which makes it easy to identify using image detection codes. Over the years, craters are created as a result of a continuous bombardment of objects from outer space like meteorites, asteroids and comets. All of them strike the lunar surface at various speeds, typically 20 kilometres per second. In addition, unlike the earth, there is no atmosphere on the moon to protect it from collision with other potential impactors.

Previous researchers such as Cheng and Ansar [5] proposed a feature detector and tracker algorithm for detecting craters as mapped landmarks and matched those using applications during EDL for the spacecrafts. In a sequence, one can also determine the position and velocity of the spacecraft using the desired parameters achieved by the matched craters technique mentioned above. For this approach, craters are classified based on their size and orientation of their outlining ellipses. There are databases of previously matched craters to detect the desired impact craters. Position is estimated using subset middle values of at least three matched craters in a linear pose estimation algorithm [6]. By combining the average velocity between two image based position and computed velocity by integrating the accelerometer reading, the actual velocity is dictated by the output of the image processing algorithm.

Continuously, there were preceding research on On-board hazard detection and avoidance for a safe landing which has aimed to autonomously detect the hazards near the landing site and determine a new landing site free from those hazards [7]. In order to detect the potential hazards on the moon's surface, there are specific requirements as agreed by the ALHAT project which will detect the hazards that are 0.3 meters tall or higher and slopes that are 5 degrees or greater mainly for the craters. Moreover, the requirement is not just to detect the hazards with the above mentioned criteria but also must be able to find a safe landing site with a diameter around 15 meters over most of the moon's surface. This proposed system is achieved by using the imaging LIDAR sensors to get the direct measurements of the lunar surface elevation from high altitude. Besides, the probability of the existence of a hazard free landing site is determined as a function of a Lander diameter, hazard map area and rock coverage, and together these procedures are used as guidance for LIDAR sensors and the overall Navigation and Control Architecture.

**Figure 1.** Terrain sensing and recognition functions for safe land site determination [5].

Hazard Detection Avoidance (HDA) and Terrain Relative Navigation (TRN) are on-board capabilities that use sensing and computing technologies to achieve optical safe and precise terrain navigation within 100 meters of a predetermined location on the lunar's surface [8]. They are based on three methods including global position estimation, local position estimation and velocity estimation as illustrated in Figure 1 above. All these functions can be realized using passive imaging or active range sensing. One of the TRN approaches is by using pattern matching which requires *a-priori* reference map describing past and future imaging and digital elevation map datasets (map-dependant system). Pattern matching approach applies landmark (Craters) matching instead of patch correlation and employs passive visible imagery system. There are several parameters required such as diameter of craters, relative distances and angles between landmarks. Craters are usually distinguished in a map of the landing site and then stored in a database. During landing process, craters are detected in descent imagery and are matched as well as compared to the database. Then only the position of the Lander is determined.

Continuous research in developing the greyscale imagery mainly on detecting landform is still being explored within these past few years. In order to detect craters of any particular planetary bodies, one of the approaches is by using the Hough Transform shape detecting assignments [9]. The proposed algorithm focuses on detection of the (sensor independent) geometric features of the impact craters (i.e centre position, craters radius) as well as identification of sensor dependant geometric features such (i.e rim height) as a following task. The use of a simple model (circular shape) for craters detection makes it possible to exploit the algorithm in different operational environments (i.e recognition of Earth and other planetary craters in the Solar System) using data attained by dissimilar sensors such as Synthetic Aperture Radar (SAR). Because of its complex algorithm, Hough Transform is not directly employed to the original image. Some pre-processing steps are necessary to obtain better result and performance of the system as illustrated in Figure 2 below. The Hough Transform has been built by Paul Hough (1962) for the identification of lines in pictures. Describing a circle represented by lines, if the radius is r and centre coordinates represent (a, b), then the parametric representation of a circle:

486 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

**Figure 1.** Terrain sensing and recognition functions for safe land site determination [5].

only the position of the Lander is determined.

Hazard Detection Avoidance (HDA) and Terrain Relative Navigation (TRN) are on-board capabilities that use sensing and computing technologies to achieve optical safe and precise terrain navigation within 100 meters of a predetermined location on the lunar's surface [8]. They are based on three methods including global position estimation, local position estimation and velocity estimation as illustrated in Figure 1 above. All these functions can be realized using passive imaging or active range sensing. One of the TRN approaches is by using pattern matching which requires *a-priori* reference map describing past and future imaging and digital elevation map datasets (map-dependant system). Pattern matching approach applies landmark (Craters) matching instead of patch correlation and employs passive visible imagery system. There are several parameters required such as diameter of craters, relative distances and angles between landmarks. Craters are usually distinguished in a map of the landing site and then stored in a database. During landing process, craters are detected in descent imagery and are matched as well as compared to the database. Then

overall Navigation and Control Architecture.

coverage, and together these procedures are used as guidance for LIDAR sensors and the

$$\mathbf{R} \text{ (x, y)} = \{ \mathbf{x} = \mathbf{a} + \mathbf{r} \cos \Theta, \mathbf{y} = \mathbf{b} + \mathbf{r} \sin \Theta \}\tag{1}$$
 
$$\text{where } \Theta = \{ 0, 2\pi \}$$

Each point (x, y) represents a, b and r parameter is mapped in a cone surface that has the following representation:

$$\mathbf{H} \text{ (a, b, r)} = \{ \mathbf{a} = \mathbf{x} - \mathbf{r} \cos \Theta, \mathbf{b} = \mathbf{y} - \mathbf{r} \sin \Theta \}\tag{2}$$
 
$$\text{where } \Theta = \{ \mathbf{0}, 2\pi \}$$
 
$$\text{WWWWWWWWWWW}\tag{3}$$

**Figure 2.** Result obtained using Hough Transform in SAR (Synthetic Aperture Radar) Image [7].

There is also multiple approach algorithms in detecting craters on the lunar's surface as proposed by Sawabe, Matsunaga and Rokugawa, 2005. It is known that the crater's feature changes according to its size. Small craters form a simple circle, and the larger its size, the more complex its shape becomes [3]. This change in feature poses difficult problems to detect craters with different sizes by a single approach. In their data-dependant based algorithm, they defined that a crater is a circular topographical feature in images and a minimum detection crater size is two pixels in radius [13] and it uses data from SELENE (Selenological and Engineering Explorer) to visualize the surface geological settings and the subsurface structure of the Lunar. These approaches are different to the authors' research as they consider the crater to bean ellipse for their detection algorithm. The authors also propose the data independent based algorithm. Four different methods were used with the crater detecting algorithm to find (1) 'shady and sunny' patterns in images with low sun angle, (2) circular features in edge images (3)curves and circles in thinned and connected edge lines, and (4)discrete or broken circular edge lines using fuzzy Hough transform. Besides, the detected craters are also classified by spectral characteristics derived from Clementine UV-Vis multi-spectral images [13]. The main advantages of the proposed algorithm compared to the previous one are that the detection algorithm is uncomplicated and it has an outstanding successful rate of detections. These methods of detection and their determination of accuracy will be evaluated in the experimental results afterwards.

In Landmark Based Pinpoint Landing Simulator (LAMPS) by Cheng and Ansar, a robust yet complex crater detection algorithm has been developed for autonomous spacecraft navigation. Based on their research, craters might have a random appearance based on their ages and sizes. For example, younger craters may have sharper and regular rims [14]. Spatial densities of craters also form the primary basis for assessing the relative and absolute ages of geological units on planetary surfaces [14]. However, typical craters will have ellipse shape in their rims, with a light to dark pattern that is dictated by the sun azimuth and elevation as well as its own topography. In fact, this statement is very similar to the authors' own approach in defining a crater as a composition of light and dark patch. Technically, Cheng and Ansar approach algorithm consists of five major steps which are edge detection, rim edge grouping, ellipse fitting, precision fitting and crater confidence evaluation. Another important property of landmark based detection system is the use of spacecraft pinpoint landing (PPL) for autonomous navigation method. To decrease the probability of landing on a hazard surface, one of the two safe landing proposals must be taken into account: craters hazard detection avoidance, which will detect all hazardous craters upon landing on the moon's surface or pinpoint landing which determines the Lander's position in real time and guide the spacecraft to a safe and free landing site, away from those hazards (craters).

According to recent studies on the size and frequency of the craters on a Mars' surface [17], a sufficient number of adequately sized craters for determining spacecraft position are very likely to be found in descent imagery. For an instance, if the image was taken using a camera field of 45 degrees and is taken from 8km above the surface, there will be an average of 94 craters of less than 200m in diameter. Ideally, from this situation, these craters can be used as landmarks to match a pre-existing crater database and therefore to determine the position of the Lander. This approach of pattern matching will be further used as future works in the authors' research. For the time being, the authors have proposed to use a projection geometry concept in determining the orientation and position of the spacecraft using two vital equations that were discussed later.

As in Figure (3) below, the proposed pinpoint landing is as follow. First, the landing site is pre-determined on the targeted body (moon's surface, Mars' surface, etc) on the earth using orbital imagery, and the landmarks within the landing ellipse (red ellipse) are mapped. During EDL, its preliminary position prior to the landmarks and selected landing site is determined. The Lander's position is then frequently tracked and guided using continuous updates of the Lander's position and the velocity all the way through the descent.

**Figure 3.** Craters pattern matching for position estimation of the spacecraft during EDL [14]
