Ayanna M. Howard

Behavior-based robot navigation on challenging terrain: A fuzzy logic approach

This paper presents a new strategy for behavior-based navigation of field mobile robots on challenging terrain, using a fuzzy logic approach and a novel measure of terrain traversability. A key feature of the proposed approach is real-time assessment of terrain characteristics and incorporation of this information in the robot navigation strategy. Three terrain characteristics that strongly affect its traversability, namely, roughness, slope, and discontinuity, are extracted from video images obtained by on-board cameras. This traversability data is used to infer, in real time, the terrain Fuzzy Rule-Based Traversability Index, which succinctly quantifies the ease of traversal of the regional terrain by the mobile robot. A new traverse-terrain behavior is introduced that uses the regional traversability index to guide the robot to the safest and the most traversable terrain region. The regional traverse-terrain behavior is complemented by two other behaviors, local avoid-obstacle and global seek-goal. The recommendations of these three behaviors are integrated through adjustable weighting factors to generate the final motion command for the robot. The weighting factors are adjusted automatically, based on the situational context of the robot. The terrain assessment and robot navigation algorithms Are implemented on a Pioneer commercial robot and field-test studies are conducted. These studies demonstrate that the robot possesses intelligent decision-making capabilities that are brought to bear in negotiating hazardous terrain conditions during the robot motion.

Vision‐based terrain characterization and traversability assessment

© 2001 John Wiley & Sons, Inc. Published online in Wiley InterScience (www.interscience.wiley.com)

A rule-based fuzzy traversability index for mobile robot navigation

This 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.

Enhancing fuzzy robot navigation systems by mimicking human visual perception of natural terrain traversability

The paper presents a technique for learning to assess terrain traversability for outdoor mobile robot navigation using human-embedded logic and real-time perception of terrain features extracted from image data. The methodology utilizes a fuzzy logic framework and vision algorithms for analysis of the terrain. The terrain assessment and learning methodology is tested and validated with a set of real world image data acquired by an onboard vision system.

An intelligent terrain-based navigation system for planetary rovers

A fuzzy logic framework for onboard terrain analysis and guidance towards traversable regions. An onboard terrain-based navigation system for mobile robots operating on natural terrain is presented. This system utilizes a fuzzy-logic framework for onboard analysis of the terrain and develops a set of fuzzy navigation rules that guide the rover toward the safest and the most traversable regions. The overall navigation strategy deals with uncertain knowledge about the environment and uses the onboard terrain analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The navigation system is tested and validated with a set of physical rover experiments and demonstrates the autonomous capability of the system.

Overtrust of Robots in Emergency Evacuation Scenarios

Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robots instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.

Multi-sensor terrain classification for safe spacecraft landing

A novel multi-sensor information fusion methodology for intelligent terrain classification is presented. The focus of this research is to analyze safety characteristics of the terrain using imagery data obtained by on-board sensors during spacecraft descent. This information can be used to enable the spacecraft to land safely on a planetary surface. The focus of our approach is on robust terrain analysis and information fusion in which the terrain is analyzed using multiple sensors and the extracted terrain characteristics are combined to select safe landing sites for touchdown. The novelty of this method is the incorporation of the T-Hazard Map, a multi-valued map representing the risk associated with landing on a planetary surface. The fusion method is explained in detail in this paper and computer simulation results are presented to validate the approach.

Intelligent learning for deformable object manipulation

The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. Our methodology relies on the implementation of two main tasks. Our first task is to calculate deformation characteristics for a non-rigid object represented by a physically-based model. Using nonlinear partial differential equations, we model the particle motion of the deformable object in order to calculate the deformation characteristics. For our second task, we must calculate the minimum force required to successfully lift the deformable object. This minimum lifting force can be learned using a technique called ‘iterative lifting’. Once the deformation characteristics and the associated lifting force term are determined, they are used to train a neural network for extracting the minimum force required for subsequent deformable object manipulation tasks. Our developed algorithm is validated with two sets of experiments. The first experimental results are derived from the implementation of the algorithm in a simulated environment. The second set involves a physical implementation of the technique whose outcome is compared with the simulation results to test the real world validity of the developed methodology.

An Integrated Approach for Achieving Multirobot Task Formations

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.

Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education

This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI.