A Rule-Based Fuzzy Traversability Index for Mobile Robot Navigation

©2001 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea, May 21-26, 2001.DOI: 10.1109/ROBOT.2001.933088This paper presents a rule-based fuzzy traversability index that quantifies the ease-of-traversal of a terrain by a mobile robot based on real-time measurements of terrain characteristics retrieved from imagery data. These characteristics include, but are not limited to slope, roughness, hardness, and discontinuity. The proposed representation of terrain traversability incorporates an intuitive, linguistic approach for expressing terrain characteristics that is robust with respect to imprecision and uncertainty in the terrain measurements. The terrain assessment method is tested and validated with a set of real-world imagery data. These tests demonstrate the capability of the terrain classification algorithm for perceiving hazards associated with terrain traversal

Approximate Reasoning for Safety and Survivability of Planetary Rovers

© 2003 Elsevier Science B.V.DOI: 10.1016/S0165-0114(02)00228-2Operational safety and health monitoring are critical matters for autonomous planetary rovers operating on remote and challenging terrain. This paper describes rover safety issues and presents an approximate reasoning approach to maintaining vehicle safety in a navigational context. The proposed rover safety module is composed of two distinct behaviors: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic implementations of these behaviors on outdoor terrain is presented. Sensing of vehicle safety coupled with visual neural network-based perception of terrain quality are used to infer safe speeds during rover traversal. In addition, approximate reasoning for self-regulation of internal operating conditions is briefly discussed. The core theoretical foundations of the applied soft computing techniques is presented and supported by descriptions of field tests and laboratory experimental results. For autonomous rovers, the approach provides intrinsic safety cognizance and a capacity for reactive mitigation of navigation risks

Designing an expert knowledge-based Systemic Importance Index for financial institutions

Defining whether a financial institution is systemically important (or not) is challenging due to (i) the inevitability of combining complex importance criteria such as institutions’ size, connectedness and substitutability; (ii) the ambiguity of what an appropriate threshold for those criteria may be; and (iii) the involvement of expert knowledge as a key input for combining those criteria. The proposed method, a Fuzzy Logic Inference System, uses four key systemic importance indicators that capture institutions’ size, connectedness and substitutability, and a convenient deconstruction of expert knowledge to obtain a Systemic Importance Index. This method allows for combining dissimilar concepts in a non-linear, consistent and intuitive manner, whilst considering them as continuous –non binary- functions. Results reveal that the method imitates the way experts them-selves think about the decision process regarding what a systemically important financial institution is within the financial system under analysis. The Index is a comprehensive relative assessment of each financial institution’s systemic importance. It may serve financial authorities as a quantitative tool for focusing their attention and resources where the severity resulting from an institution failing or near-failing is estimated to be the greatest. It may also serve for enhanced policy-making (e.g. prudential regulation, oversight and supervision) and decision-making (e.g. resolving, restructuring or providing emergency liquidity).Systemic Importance, Systemic Risk, Fuzzy Logic, Approximate Reasoning, Too-connected-to-fail, Too-big-to-fail. Classification JEL: D85, C63, E58, G28.

Macro-Prudential Assessment of Colombian Financial Institutions’ Systemic Importance

Three metrics are designed to assess Colombian financial institutions’ size, connectedness and non-­substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large-value payment system network, and (iii) asset value of core financial services. Two systemic importance indexes are calculated based on two different aggregation methods for the three metrics: fuzzy logic and principal component analysis. The resulting indexes are complementary and provide a comprehensive relative assessment of each financial institution’s systemic importance in the Colombian case, in which the choice of metrics pursues the macro-­prudential perspective of financial stability. They both (i) agree on the skewed (i.e. inhomogeneous) nature of systemic importance and its approximate scale-­free distribution; (ii) on the preeminence of credit institutions as the main contributors to systemic importance, and (iii) on the non-­‐trivial importance of a few non-­‐banking institutions

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

Terrain parameter estimation and traversability assessment for mobile robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (leaves 65-68).The estimation of terrain characteristics is an important missions of Martian exploration rovers. Since only limited resources and human supervision are available, efficient and autonomous method of estimation are required. In this thesis, an on-line estimation method of two important terrain parameters, cohesion and internal friction angle, is developed. The method uses onboard rover sensors and is computationally efficient. Terrain parameter estimation is of scientific interest, and can also be useful in predicting rover mobility on rough-terrain. A method to estimate traversability of a rover on deformable terrain using on-board sensors is presented. Simulation and experimental results show that the proposed methods can accurately and efficiently estimate traversability of deformable terrain.by Shinwoo Kang.S.M

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Methodology for prototyping increased levels of automation for spacecraft rendezvous functions

The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to program requirements for automation, including Automated Rendezvous and Docking (AR&D). Studies of spacecraft development often point to the locus of decision-making authority between humans and computers (i.e. automation) as a prime driver for cost, safety, and mission success. Therefore, a critical component in the CEV development is the determination of the correct level of automation. To identify the appropriate levels of automation and autonomy to design into a human space flight vehicle, NASA has created the Function-specific Level of Autonomy and Automation Tool (FLOAAT). This research develops a methodology for prototyping increased levels of automation for spacecraft rendezvous functions. This methodology was used to evaluate the accuracy of the FLOAAT-specified levels of automation, via prototyping. Two spacecraft rendezvous planning tasks were selected and then prototyped in Matlab using Fuzzy Logic (FL) techniques and existing Shuttle rendezvous trajectory algorithms. The prototyped functions are the determination of the maximum allowable Timeof- IGnition (TIG) slip for a rendezvous phasing burn and the evaluation of vehicle position relative to Transition initiation (Ti) position constraints. The methodology for prototyping rendezvous functions at higher levels of automation is judged to be a promising technique. The results of the prototype indicate that the FLOAAT recommended level of automation is reasonably accurate and that FL can be effectively used to model human decision-making used in spacecraft rendezvous. FL has many desirable attributes for modeling human decision-making, which makes it an excellent candidate for additional spaceflight automation applications. These conclusions are described in detail as well as recommendations for future improvements to the FLOAAT method and prototyped rendezvous functions

Terrain Aware Traverse Planning for Mars Rovers

NASA is proposing a Mars Sample Return mission, to be completed within one Martian year, that will require enhanced autonomy to perform its duties faster, safer, and more efficiently. With its main purpose being to retrieve samples possibly tens of kilometers away, it will need to drive beyond line-of-sight to get to its target more quickly than any rovers before. This research proposes a new methodology to support a sample return mission and is divided into three compo-nents: map preparation (map of traversability, i.e., ability of a terrain to sustain the traversal of a vehicle), path planning (pre-planning and replanning), and terrain analysis. The first component aims at creating a better knowledge of terrain traversability to support planning, by predicting rover slip and drive speed along the traverse using orbital data. By overlapping slope, rock abundance and terrain types at the same location, the expected drive velocity is obtained. By combining slope and thermal data, additional information about the experienced slip is derived, indicating whether it will be low (less than 30%) or medium to high (more than 30%). The second component involves planning the traverse for one Martian day (or sol) at a time, based on the map of expected drive speed. This research proposes to plan, offline, several paths traversable in one sol. Once online, the rover chooses the fastest option (the path cost being calculated using the distance divided by the expected velocity). During its drive, the rover monitors the terrain via analysis of its experienced wheel slip and actual speed. This information is then passed along the different pre-planned paths over a given distance (e.g., 25 m) and the map of traversability is locally updated given this new knowledge. When an update occurs, the rover calculates the new time of arrival of the various paths and replans its route if necessary. When tested in a simulation study on maps of the Columbia Hills, Mars, the rover successfully updates the map given new information drawn from a modified map used as ground truth for simulation purposes and replans its traverse when needed. The third component describes a method to assess the soil in-situ in case of dangerous terrain detected during the map update, or if the monitoring is not enough to confirm the traversability predicted by the map. The rover would deploy a shear vane instrument to compute intrinsic terrain parameters, information then propagated ahead of the rover to update the map and replan if necessary. Experiments in a laboratory setting as well as in the field showed promising results, the mounted shear vane giving values close to the expected terrain parameters of the tested soils