Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A* is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan one to two orders of magnitudes faster than planning from scratch since it modifies previous search results locally. Consequently, it has been extensively used in mobile robotics. In this paper, we introduce an alternative to Focussed Dynamic A* that implements the same navigation strategy but is algorithmically different. Focussed Dynamic A* Lite is simpler, easier to understand, easier to analyze and easier to extend than Focussed Dynamic A*, yet is more efficient. We believe that our results will make D*-like replanning algorithms even more popular and enable robotics researchers to adapt them to additional applications.

/pdf/improved-fast-replanning-for-robot-navigation-in-unknown-2pz3p60jfw.pdf

Improved fast replanning for robot navigation in unknown terrain

Tracked mobile robots in the 20 kg size class are under development for applications in urban reconnaissance. For efficient deployment, it is desirable for teams of robots to be able to automatically execute path following behaviors, with one or more followers tracking the path taken by a leader. The key challenges to enabling such a capability are (1) to develop sensor packages for such small robots that can accurately determine the path of the leader and (2) to develop path following algorithms for the subsequent robots. To date, we have integrated gyros, accelerometers, compass/inclinometers, odometry, and differential GPS into an effective sensing package. The paper describes the sensor package, sensor processing algorithm and path tracking algorithm we have developed for the leader/follower problem in small robots and shows the results of performance characterization of the system. We also document pragmatic lessons learned about design, construction, and electromagnetic interference issues particular to the performance of state sensors on small robots.

/pdf/algorithms-and-sensors-for-small-robot-path-following-11wl833k31.pdf

Algorithms and sensors for small robot path following

Chang’E-4 (CE-4) was the first mission to accomplish the goal of a successful soft landing on the lunar farside. The landing trajectory and the location of the landing site can be effectively reconstructed and determined using series of images obtained during descent when there were no Earth-based radio tracking and the telemetry data. Here we reconstructed the powered descent trajectory of CE-4 using photogrammetrically processed images of the CE-4 landing camera, navigation camera, and terrain data of Chang’E-2. We confirmed that the precise location of the landing site is 177.5991°E, 45.4446°S with an elevation of −5935 m. The landing location was accurately identified with lunar imagery and terrain data with spatial resolutions of 7 m/p, 5 m/p, 1 m/p, 10 cm/p and 5 cm/p. These results will provide geodetic data for the study of lunar control points, high-precision lunar mapping, and subsequent lunar exploration, such as by the Yutu-2 rover. The Chang’E-4 mission in January 2019 had the major challenge to land on the lunar far side without traditional radiometric techniques due to the missing line-of-sight. The authors here describe landing trajectory reconstruction and positioning techniques based upon the Moon’s digital terrain model that allowed reproducing the entire process of a successful landing.

Descent trajectory reconstruction and landing site positioning of Chang'E-4 on the lunar farside.

Keywords: computer vision ; USAR ; sensor fusion ; [VRAI] Note: Swiss Federal Institute of Technology Reference LSRO2-REPORT-2004-002 Record created on 2005-02-04, modified on 2017-05-10

Human detection for robotic urban search and rescue

Visual terrain mapping for Mars exploration

https://robotics.usc.edu/publications/downloads/pub/244/

A Portable, Autonomous, Urban Reconnaissance Robot

In the exploration of the planets of our solar system, images taken during a lander's descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but they also provide progressively higher-resolution maps for mission planning. This paper addresses the generation of depth maps from the descent images. Our approach has two steps, motion refinement and depth recovery. During motion refinement, we use an initial motion estimate in order to avoid the intrinsic motion ambiguity. The objective of the motion-refinement step is to adjust the motion parameters such that the reprojection error is minimized. The depth-recovery step correlates adjacent frames to match pixels for triangulation. Due to the descending motion, the conventional rectification process is replaced by a set of anti-aliasing image warpings corresponding to a set of virtual parallel planes. We demonstrate experimental results on synthetic and real descent images.

Computing depth maps from descent images

In the exploration of the other planets of our solar system, images taken during a lander's descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but also provide progressively higher-resolution maps for mission planning. The paper addresses the generation of depth maps from the descent images. Our approach has two steps: motion refinement and depth recovery. During motion refinement, we use an initial motion estimate in order to avoid the intrinsic ambiguity in descending motions. The objective of the motion refinement step is to adjust the motion parameters such that the epipolar constraints are valid between adjacent frames. The depth recovery step correlates adjacent frames to match pixels for triangulation. Due to the descending motion, the conventional rectification process is replaced by a set of anti-aliasing image warpings corresponding to a set of virtual parallel planes. We demonstrate experimental results on synthetic and real descent images.

https://faculty.washington.edu/cfolson/papers/pdf/mva05.pdf

Yalin Xiong

Papers

A Portable, Autonomous, Urban Reconnaissance Robot

Computing depth maps from descent images

Computing depth maps from descent imagery