In this paper we try to develop an algorithm for visual obstacle avoidance of autonomous mobile robot. The input of the algorithm is an image sequence grabbed by an embedded camera on the B21r robot in motion. Then, the optical flow information is extracted from the image sequence in order to be used in the navigation algorithm. The optical flow provides very important information about the robot environment, like: the obstacles disposition, the robot heading, the time to collision and the depth. The strategy consists in balancing the amount of left and right side flow to avoid obstacles, this technique allows robot navigation without any collision with obstacles. The robustness of the algorithm will be showed by some

https://journals.sagepub.com/doi/pdf/10.5772/5715

Optical Flow Based Robot Obstacle Avoidance

The present work considers the development of a wheelchair for people with special needs, which is capable of navigating semi-autonomously within its workspace. This system is expected to prove useful to people with impaired mobility and limited fine motor control of the upper extremities. Among the implemented behaviors of this robotic system are the avoidance of obstacles, the motion in the middle of the free space and the following of a moving target specified by the user (e.g., a person walking in front of the wheelchair). The wheelchair is equipped with sonars, which are used for distance measurement in preselected critical directions, and with a panoramic camera with a 360 degree field of view, which is used for following a moving target. After suitably processing the color sequence of the panoramic images using the color histogram of the desired target, the orientation of the target with respect to the wheelchair is determined, while its distance is determined by the sonars. The motion control laws developed for the system use the sensory data and take into account the non-holonomic kinematic constraints of the wheelchair, in order to guarantee certain desired features of the closed-loop system, such as stability. Moreover, they are as simplified as possible to minimize implementation requirements. An experimental prototype has been developed at ICS–FORTH, based on a commercially-available wheelchair. The sensors, the computing power and the electronics needed for the implementation of the navigation behaviors and of the user interfaces (touch screen, voice commands) were developed as add-on modules and integrated with the wheelchair.

/pdf/semi-autonomous-navigation-of-a-robotic-wheelchair-15kewiusvk.pdf

Semi-autonomous Navigation of a Robotic Wheelchair

We investigate the deployment of a submersible platform with stereovision imaging capability for three-dimensional (3D) mapping of benthic habitats and other sea-floor structures over local areas. A complete framework is studied, comprising: 1) suitable trajectories to be executed for data collection; 2) data processing for positioning and trajectory followed by online frame-to-frame and frame-to-mosaic registration of images, as well as recursive global realignment of positions along the path; and 3) 3D mapping by the fusion of various visual cues, including motion and stereo within a Kalman filter. The computational requirements of the system are evaluated, formalizing how processing may be achieved in real time. The proposed scenario is simulated for testing with known ground truth to assess the system performance, to quantify various errors, and to identify how performance may be improved. Experiments with underwater images are also presented to verify the performance of various components and the overall scheme.

Stereovision imaging on submersible platforms for 3-D mapping of benthic habitats and sea-floor structures

A reactive robotic centering behavior based on panoramic vision is presented. It is inspired by the way insects exploit visual information in analogous navigation tasks. By employing a panoramic camera, the development of the centering behavior is simplified both from a theoretical and from an implementation point of view. The proposed method relies on the extraction of primitive visual information from appropriately selected areas of a panoramic visual field and its direct use in the control law. Experimental results from an implementation of this method on a robotic platform demonstrate a centering behavior which can be achieved in real-time and with high accuracy. The proposed technique circumvents the need to address complex problems of 3D structure estimation and the resulting control laws were shown to possess the required stability properties.

Biomimetic centering behavior [mobile robots with panoramic sensors]

Image analysis and computer vision: 1999

In this paper we present a new method for vision-based, reactive robot navigation that enables a robot to move in the middle of the free space by exploiting both central and peripheral vision. The robot employs a forward-looking camera for central vision and two side-looking cameras for sensing the periphery of its visual field. The developed method combines the information acquired by this trinocular vision system and produces low-level motor commands that keep the robot in the middle of the free space. The approach follows the purposive vision paradigm in the sense that vision is not studied in isolation but in the context of the behaviors that the system is engaged as well as the environment and the robot's motor capabilities. It is demonstrated that by taking into account these issues, vision processing can be drastically simplified still giving rise to quite complex behaviors. The proposed method does not make strict assumptions about the environment, requires very low level information to be extracted from the images, produces a robust robot behavior and is computationally efficient. Results obtained by bath simulations and from a prototype on-line implementation demonstrate the effectiveness of the method.

/pdf/combining-central-and-peripheral-vision-for-reactive-robot-1z7ojik4iq.pdf

F. Bergholm

Papers

Combining central and peripheral vision for reactive robot navigation