Vision-based Estimation of Slip Angle for Mobile Robots and Planetary Rovers

2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 19-23, 200

Inspection with Robotic Microscopic Imaging

Future Mars rover missions will require more advanced onboard autonomy for increased scientific productivity and reduced mission operations cost. One such form of autonomy can be achieved by targeting precise science measurements to be made in a single command uplink cycle. In this paper we present an overview of our solution to the subproblems of navigating a rover into place for microscopic imaging, mapping an instrument target point selected by an operator using far away science camera images to close up hazard camera images, verifying the safety of placing a contact instrument on a sample or finding nearby safe points, and analyzing the data that comes back from the rover. The system developed includes portions used in the Multiple Target Single Cycle Instrument Placement demonstration at NASA Ames in October 2004, and portions of the MI Toolkit delivered to the Athena Microscopic Imager Instrument Team for the MER mission still operating on Mars today. Some of the component technologies are also under consideration for MSL mission infusion

Vision-based estimation of slip angle for mobile robots and planetary rovers

Abstract — For a mobile robot it is critical to detect and compensate for slippage, especially when driving in rough terrain environments. Due to its highly unpredictable nature, drift largely affects the accuracy of localization and control systems, even leading, in extreme cases, to the danger of vehicle entrapment with consequent mission failure. This paper presents a novel method for lateral slip estimation based on visually observing the trace produced by the wheels of the robot, during traverse of soft, deformable terrain, as that expected for lunar and planetary rovers. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel trace from the planned path of the robot suggests occurrence of sideslip that can be detected, and more interestingly, measured. This allows one to estimate the actual heading angle of the robot, usually referred to as the slip angle. The details of the various steps of the visual algorithm are presented and the results of experimental tests performed in the field with an all-terrain rover are shown, proving the method to be effective and robust. I

Method and system for providing autonomous control of a platform

The present application provides a system for enabling instrument placement from distances on the order of five meters, for example, and increases accuracy of the instrument placement relative to visually-specified targets. The system provides precision control of a mobile base of a rover and onboard manipulators (e.g., robotic arms) relative to a visually-specified target using one or more sets of cameras. The system automatically compensates for wheel slippage and kinematic inaccuracy ensuring accurate placement (on the order of 2 mm, for example) of the instrument relative to the target. The system provides the ability for autonomous instrument placement by controlling both the base of the rover and the onboard manipulator using a single set of cameras. To extend the distance from which the placement can be completed to nearly five meters, target information may be transferred from navigation cameras (used for long-range) to front hazard cameras (used for positioning the manipulator)

A Phase Based Dense Stereo Algorithm Implemented in CUDA

Stereo imaging is routinely used in Simultaneous Localization and Mapping (SLAM) systems for the navigation and control of autonomous spacecraft proximity operations, advanced robotics, and robotic mapping and surveying applications. A key step (and generally the most computationally expensive step) in the generation of high fidelity geometric environment models from image data is the solution of the dense stereo correspondence problem. A novel method for solving the stereo correspondence problem to sub-pixel accuracy in the Fourier frequency domain by exploiting the Convolution Theorem is developed. The method is tailored to challenging aerospace applications by incorporation of correction factors for common error sources. Error-checking metrics verify correspondence matches to ensure high quality depth reconstructions are generated. The effect of geometric foreshortening caused by the baseline displacement of the cameras is modeled and corrected, drastically improving correspondence matching on highly off-normal surfaces. A metric for quantifying the strength of correspondence matches is developed and implemented to recognize and reject weak correspondences, and a separate cross-check verification provides a final defense against erroneous matches. The core components of this phase based dense stereo algorithm are implemented and optimized in the Compute Uni ed Device Architecture (CUDA) parallel computation environment onboard an NVIDIA Graphics Processing Unit (GPU). Accurate dense stereo correspondence matching is performed on stereo image pairs at a rate of nearly 10Hz

Continuous observation planning for autonomous exploration

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 233-239).Many applications of autonomous robots depend on the robot being able to navigate in real world environments. In order to navigate or path plan, the robot often needs to consult a map of its surroundings. A truly autonomous robot must, therefore, be able to drive about its environment and use its sensors to build a map before performing any tasks that require this map. Algorithms that control a robot's motion for the purpose of building a map of an environment are called autonomous exploration algorithms. Because resources such as time and energy are highly constrained in many mobile robot missions, a key requirement of autonomous exploration algorithms is that they cause the robot to explore efficiently. Planning paths to candidate observation points that will lead to efficient exploration is challenging, however, because the set of candidates, and, therefore, the robot's plan, change frequently as the robot adds information to the map. The main claim of this thesis is that, in situations in which the robot discerns the large scale structure of the environment early on during its exploration, the robot can produce paths that cause it to explore efficiently by planning observations to make over a finite horizon. Planning over a finite horizon entails finding a path that visits candidates with the maximum possible total utility, subject to the constraint that the path cost is less than a given threshold value. Finding such a path corresponds to solving the Selective Traveling Salesman Problem (S-TSP) over the set of candidates.(cont.) In this thesis, we evaluate our claim by implementing full horizon, finite horizon, and greedy approaches to planning observations, and comparing the efficiency of these approaches in both real and simulated environments. In addition, we develop a new approach for solving the S-TSP by framing it as an Optimal Constraint Satisfaction Problem (OCSP).by Bradley R. Hasegawa.M.Eng