Editorial: Special issue on ground robots operating in dynamic, unstructured and large-scale outdoor environments

Real-world outdoor applications of ground robots have, to date, been limited primarily to remote inspection of suspected explosive devices and, with less success, to the broader domain of remote survey and inspection in hazardous environments. Such robots have almost exclusively been tele-operated. Also notable as examples of outdoor ground robots are the planetary rovers, currently deployed with great success on the surface of Mars. But with the rapid development of autonomous (driverless) cars, and the emergence of robotic vehicles in agriculture, it is likely that there will be significant growth in both the numbers and scope of commercial ground robots in outdoor environments in the near future.For this special issue we called for papers that present land robot systems deployed in the field in similar realistic challenges. We sought papers that focus on any aspect of robotic systems, from vehicle design to the overall system architecture and control, via terrain mapping, localization, mission planning and execution – with an emphasis on systems that fulfil a specific real world task. We specified that robot or system innovations must be supported by extensive field results. Also that field tests must be under realistic and challenging conditions with respect to the terrain type, the scenario to be achieved, and/or the conditions within which the scenarios must be achieved

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground

A learning approach to swarm-based path detection and tracking

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThis dissertation presents a set of top-down modulation mechanisms for the modulation of the swarm-based visual saliency computation process proposed by Santana et al. (2010) in context of path detection and tracking. In the original visual saliency computation process, two swarms of agents sensitive to bottom-up conspicuity information interact via pheromone-like signals so as to converge on the most likely location of the path being sought. The behaviours ruling the agents’motion are composed of a set of perception-action rules that embed top-down knowledge about the path’s overall layout. This reduces ambiguity in the face of distractors. However, distractors with a shape similar to the one of the path being sought can still misguide the system. To mitigate this issue, this dissertation proposes the use of a contrast model to modulate the conspicuity computation and the use of an appearance model to modulate the pheromone deployment. Given the heterogeneity of the paths, these models are learnt online. Using in a modulation context and not in a direct image processing, the complexity of these models can be reduced without hampering robustness. The result is a system computationally parsimonious with a work frequency of 20 Hz. Experimental results obtained from a data set encompassing 39 diverse videos show the ability of the proposed model to localise the path in 98.67 % of the 29789 evaluated frames

Efficient vanishing point detection method in unstructured road environments based on dark channel prior

Vanishing point detection is a key technique in the fields such as road detection, camera calibration and visual navigation. This study presents a new vanishing point detection method, which delivers efficiency by using a dark channel prior‐based segmentation method and an adaptive straight lines search mechanism in the road region. First, the dark channel prior information is used to segment the image into a series of regions. Then the straight lines are extracted from the region contours, and the straight lines in the road region are estimated by a vertical envelope and a perspective quadrilateral constraint. The vertical envelope roughly divides the whole image into sky region, vertical region and road region. The perspective quadrilateral constraint, as the authors defined herein, eliminates the vertical lines interference inside the road region to extract the approximate straight lines in the road region. Finally, the vanishing point is estimated by the meanshift clustering method, which are computed based on the proposed grouping strategies and the intersection principles. Experiments have been conducted with a large number of road images under different environmental conditions, and the results demonstrate that the authors’ proposed algorithm can estimate vanishing point accurately and efficiently in unstructured road scenes

Simulated road following using neuroevolution

This paper describes a methodology wherein genetic algorithms were used to evolve neural network controllers for application in automatic road driving. The simulated controllers were capable of dynamically varying the mixture of colour components in the input image to ensure the ability to perform well across the entire range of possible environments. During the evolution phase, they were evaluated in a set of environments carefully designed to encourage the development of flexible and general-purpose solutions. Successfully evolved controllers were capable of navigating simulated roads across challenging test environments, each with different geometric and colour distribution properties. These controllers proved to be more robust and adaptable compared to the previous work done using this evolutionary approach. This was due to their improved dynamic colour perception capabilities, as they were now able to demonstrate feature extraction in three (red, green and blue) colour channels