Autonomous Navigation Tasks Research Articles

Path planning of wheeled mobile robot with simultaneous free space locating capability

This paper presents a hybrid path planning algorithm for the design of autonomous vehicles such as mobile robots. The hybrid planner is based on Potential Field method and Voronoi Diagram approach and is represented with the ability of concurrent navigation and map building. The system controller (Look-ahead Control) with the Potential Field method guarantees the robot generate a smooth and safe path to an expected position. The Voronoi Diagram approach is adopted for the purpose of helping the mobile robot to avoid being trapped by concave environment while exploring a route to a target. This approach allows the mobile robot to accomplish an autonomous navigation task with only an essential exploration between a start and goal position. Based on the existing topological map the mobile robot is able to construct sub-goals between predefined start and goal, and follows a smooth and safe trajectory in a flexible manner when stationary and moving obstacles co-exist.

Omnidirectional vision and conformal geometric algebra for visual landmark identification

SUMMARYThe automatic landmark identification is very important in autonomous robot navigation tasks. In this paper, we use a monocular omnidirectional vision system to extract the image features and the conformal geometric algebra to compute the projective invariants from such features. We show how these features can be used to compute projective and permutationp2-invariantsfrom any kind of omnidirectional vision system. Thep2-invariantsrepresent scene sublandmarks, and a set of them characterize a landmark. The advantage of this representation is that the landmarks are more robust than the single cross-ratio.

Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments

This paper presents a novel 3D-model-based computer-vision method for tracking the full six degree-of-freedom (dof) pose (position and orientation) of a rigid body, in real-time. The methodology has been targeted for autonomous navigation tasks, such as interception of or rendezvous with mobile targets. Tracking an object's complete six-dof pose makes the proposed algorithm useful even when targets are not restricted to planar motion (e.g., flying or rough-terrain navigation). Tracking is achieved via a combination of textured model projection and optical flow. The main contribution of our work is the novel combination of optical flow with z-buffer depth information that is produced during model projection. This allows us to achieve six-dof tracking with a single camera. A localized illumination normalization filter also has been developed in order to improve robustness to shading. Real-time operation is achieved using GPU-based filters and a new data-reduction algorithm based on colour-gradient redundancy, which was developed within the framework of our project. Colour-gradient redundancy is an important property of colour images, namely, that the gradients of all colour channels are generally aligned. Exploiting this property provides a threefold increase in speed. A processing rate of approximately 80 to 100 fps has been obtained in our work when utilizing synthetic and real target-motion sequences. Sub-pixel accuracies were obtained in tests performed under different lighting conditions.

Decisional autonomy of planetary rovers

AbstractTo achieve the ever increasing demand for science return, planetary exploration rovers require more autonomy to successfully perform their missions. Indeed, the communication delays are such that teleoperation is unrealistic. Although the current rovers (such as MER) demonstrate a limited navigation autonomy, and mostly rely on ground mission planning, the next generation (e.g., NASA Mars Science Laboratory and ESA Exomars) will have to regularly achieve long range autonomous navigation tasks. However, fully autonomous long range navigation in partially known planetary‐like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build adequate representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions, and to localize itself as it moves. All these activities have to be planned, scheduled, and performed according to the rover context, and controlled so that the mission is correctly fulfilled. To achieve these objectives, we have developed a temporal planner and an execution controller, which exhibit plan repair and replanning capabilities. The planner is in charge of producing plans composed of actions for navigation, science activities (moving and operating instruments), communication with Earth and with an orbiter or a lander, while managing resources (power, memory, etc.) and respecting temporal constraints (communication visibility windows, rendezvous, etc.). High level actions also need to be refined and their execution temporally and logically controlled. Finally, in such critical applications, we believe it is important to deploy a component that protects the system against dangerous or even fatal situations resulting from unexpected interactions between subsystems (e.g., move the robot while the robot arm is unstowed) and/or software components (e.g., take and store a picture in a buffer while the previous one is still being processed). In this article we review the aforementioned capabilities, which have been developed, tested, and evaluated on board our rovers (Lama and Dala). After an overview of the architecture design principle adopted, we summarize the perception, localization, and motion generation functions required by autonomous navigation, and their integration and concurrent operation in a global architecture. We then detail the decisional components: a high level temporal planner that produces the robot activity plan on board, and temporal and procedural execution controllers. We show how some failures or execution delays are being taken care of with online local repair, or replanning. © 2007 Wiley Periodicals, Inc.

Active vision-based control schemes for autonomous navigation tasks

This paper deals with active-vision-based practical control schemes for collision avoidance as well as maintenance of clearance in a-priori unknown textured environments. These control schemes employ a visual motion cue, called the visual threat cue (VTC) as a sensory feedback signal to accomplish the desired tasks. The VTC provides some measure for a relative change in range as well as clearance between a 3D surface and a moving observer. It is a collective measure obtained directly from the raw data of gray level images, is independent of the type of 3D surface texture. It is measured in [time −1] units and needs no 3D reconstruction. The control schemes are based on a set of If–Then fuzzy rules with almost no knowledge about the vehicle dynamics, speed, heading direction, etc. They were implemented in real-time using a 486-based Personal Computer and a camera capable of undergoing 6-DOF motion.

Novel Active Vision-Based Visual Threat Cue for Autonomous Navigation Tasks

This paper deals with a novel vision-based motion cue called the Visual Threat Cue (VTC), suitable for autonomous navigation tasks such as collision avoidance and maintenance of clearance. The VTC is atime-basedscalar parameter that provides some measure for arelative change in rangeas well asclearancebetween a 3D surface and amovingobserver. It is independent of the 3D environment around the observer and needs almost noa prioriknowledge about it. A practical method to extract the VTC from a sequence of images of 3D textured surfaces is also presented. It is based on the extraction of a global image dissimilarity measure called the Image Quality Measure (IQM), which is extracted directly from the raw data of the gray level images. Based on the relative variations of the measured IQM, the VTC is extracted. Several experimental results are also presented.

Experimental Setup for Multiple Agent Interaction

To perform autonomous navigation research, a complete development system is needed. Such a system should be easy to use, modifiable, upgradable and above all allow ease of experimentation. A setup consisting of both actual modular hardware agents and simulation software is presented here. The performance and flexibility of the system is demonstrated via an autonomous navigation task of intercepting moving objects.

Qualitative landmark recognition using visual cues

Understanding the surrounding scene and identifying man-made structures is an important task in autonomous vehicle navigation in an outdoor environment. The ability to generalize the task of landmark recognition to a variety of landmarks belonging to a particular class is a difficult problem compounded by its ill-defined nature. In this paper, we propose a technique for performing landmark recognition based on visual cues of texture, edges and functional form. We use a neural network based classifier to identify regions of interest and extract linear and curved edge features falling in these areas. We combine these cues using heuristics and inference based on the function of the landmark.

A reinforcement learning approach based on the fuzzy min-max neural network

The fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. Two versions have been proposed: for supervised and unsupervised learning. In this paper a modified approach is presented that is appropriate for reinforcement learning problems with discrete action space and is applied to the difficult task of autonomous vehicle navigation when no a priori knowledge of the enivronment is available. Experimental results indicate that the proposed reinforcement learning network exhibits superior learning behavior compared to conventional reinforcement schemes.