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Virtual globes, i.e., geobrowsers that integrate multi-scale and temporal data from various sources and are based on a globe metaphor, have developed into serious tools that practitioners and various stakeholders in landscape and community planning have started using. Although these tools originate from Geographic Information Systems (GIS), they have become a different, potentially interactive and public tool set, with their own specific limitations and new opportunities. Expectations regarding their utility as planning and community engagement tools are high, but are tempered by both technical limitations and ethical issues [1,2]. Two grassroots campaigns and a collaborative visioning process, the Kimberley Climate Adaptation Project case study (British Columbia), illustrate and broaden our understanding of the potential benefits and limitations associated with the use of virtual globes in participatory planning initiatives. Based on observations, questionnaires and in-depth interviews with stakeholders and community members using an interactive 3D model of regional climate change vulnerabilities, potential impacts, and possible adaptation and mitigation scenarios in Kimberley, the benefits and limitations of virtual globes as a tool for participatory landscape planning are discussed. The findings suggest that virtual globes can facilitate access to geospatial information, raise awareness, and provide a more representative virtual landscape than static visualizations. However, landscape is not equally representative at all scales, and not all types of users seem to benefit equally from the tool. The risks of misinterpretation can be managed by integrating the application and interpretation of virtual globes into face-to-face planning processes

Multi-legged walking robots are suitable platforms for unstructured and rough terrains because of their immense locomotion capabilities. These are, however, redeemed by more sophisticated control and energy-demanding motion in comparison to wheeled robots. Particularly, electrically actuated multi-legged walking robots suffer from the adverse ratio between the robot body weight and payload capacity. Moreover, the ratio of the locomotion speed and endurance is far from what can be achieved with wheeled robots. In this paper, we focus on six-legged walking robots with statically-stable gait. Based on the analysis of existing solutions, we propose a novel construction of the affordable electrically actuated robot with substantial improvements in its motion capabilities, locomotion speed, reliability, and endurance. The proposed design is implemented in a Hexapod Ant Robot (HAntR) that is accompanied by the developed locomotion control approach to improve its rough terrains negotiation capabilities by the active distribution of the robot weight to the legs in the stance phase. Properties of the robot have been experimentally verified in extensive deployments, and based on the experimental benchmarking of the built prototype, HAntR is capable of locomotion for over an hour with the payload of 85% of its weight, and its maximum crawled distance per one second is 87 % of its nominal length. HAntR represents significant improvements not only regarding the robots with identical actuators but also in comparison to other existing platforms. Therefore, we consider the robot HAntR represents a step further towards a wide range of future applications and deployments of six-legged walking robots.

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We present a complete hardware and software solution of an FPGA-based computer vision embedded module capable of carrying out SURF image features extraction algorithm. Aside from image analysis, the module embeds a Linux distribution that allows to run programs specifically tailored for particular applications. The module is based on a Virtex-5 FXT FPGA which features powerful configurable logic and an embedded PowerPC processor. We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. The module's overall performance is evaluated and compared to CPU and GPU based solutions. Results show that the embedded module achieves comparable disctinctiveness to the SURF software implementation running in a standard CPU while being faster and consuming significantly less power and space. Thus, it allows to use the SURF algorithm in applications with power and spatial constraints, such as autonomous navigation of small mobile robots.

In this paper, we address motion efficiency in autonomous robot exploration with multi-legged walking robots that can traverse rough terrains at the cost of lower efficiency and greater body vibration. We propose a robotic system for online and incremental learning of the terrain traversal cost that is immediately utilized to reason about next navigational goals in building spatial model of the robot surrounding. The traversal cost experienced by the robot is characterized by incrementally constructed Gaussian Processes using Bayesian Committee Machine. During the exploration, the robot builds the spatial terrain model, marks untraversable areas, and leverages the Gaussian Process predictive variance to decide whether to improve the spatial model or decrease the uncertainty of the terrain traversal cost. The feasibility of the proposed approach has been experimentally verified in a fully autonomous deployment with a hexapod walking robot.