In this paper, a real-time railway fastener detection system using a high-speed laser range finder camera is presented. First, an extensive analysis of various methods based on pixel-wise and histogram similarities are conducted on a specific railway route. Then, a fusing stage is introduced which combines least correlated approaches also considering the performance upgrade after fusing. Then, the resulting method is tested on a larger database collected from a different railway route. After observing repeated successes, the method is implemented on NI LabVIEW and run real-time with a high-speed 3-D camera placed under a railway carriage designed for railway quality inspection.

Unmanned aerial vehicles (UAV) have been serving people, fulfilling various roles in research, industry and military, not to exclude personal entertainment. They have also been in use more often for malicious purposes, such as annoying neighbors, disrupting air-travel, attacking people or infrastructure. Therefore, motivated particularly by malicious uses, the research community has been interested in proposing solutions for detection of UAVs. In this work, we present a ground based aerial target detection system using lidars, relying on sparse detections rather than dense point clouds, with the aim of not only detecting but also estimating their motion and active tracking. We lay a theoretical groundwork to analyse the performance of such a lidar based detection system and help understanding its limitations. The work is complemented with field experiments utilizing a state-of-the-art lidar and several drones of different sizes, showing effectiveness in both detection and tracking using sparse measurements.

Drones are being used more and more often for various human and industry friendly applications. However, evil intended users also have access to drones and do not hesitate to use them for harmful purposes, eventually hurting humans and the economy. In this work we present an active drone detection system that uses a millimeter wave radar mounted on a drone aiming to detect, track and pursue target drones. The pursuer drone utilizes a 2D radar, and therefore a method to estimate 3D position of the target drone using 2D measurements is also presented in this paper. The method was validated and its effectiveness was tested on the field using two drones of different sizes and a commercial off-the-shelf radar.

The power consumed by skid-steered mobile robots varies enormously depending on their operating regimes and environments. Therefore, energy optimal planning of field missions can be accomplished only if the operating environment and an accurate model of the robot’s power consumption are known in advance. This paper studies influences of location of the center of mass, surface type, terrain relief, platform geometry, radius of curvature, speed, and temperature on the power consumption of a skid-steered mobile platform and derives a physics-based power model that uses friction to accurately model the power consumed for the most relevant parameters inside a wide range of operating conditions. This paper also shows how friction can be measured using power consumed by the motors and how motor constants can be measured in a noninvasive end-to-end manner. Our findings were validated with a Clearpath’s Husky A200 platform, driving it indoors and outdoors on different surfaces, varying the speed, the radius of curvature, the mass, and the center of mass location, resulting in a dense dataset on which the derived model as well as the temperature dependence of the power consumption was verified.

Coverage Path Planning (CPP) is an essential problem in many applications of robotics, including but not limited to autonomous demining and farming. Most works on CPP address time efficiency or coverage completeness in a bi-dimensional and flat environment, not taking the terrain relief into account. In this paper we use a Genetic Algorithm to optimize the solution to the CPP problem in terms of energy consumption, taking into account the constraints of natural terrains: obstacles and relief. Simulation results show that our approach is effective in reducing energy consumption of a mobile robot performing CPP.

Energy autonomy is an important aspect that needs to be improved in order to increase efficiency in mobile robotic tasks. Having accurate power models allows the estimation of energy consumption along different trajectories. This article proposes a power model for two-wheel differential drive mobile robots. The proposed model takes into account the dynamic parameters of the robot and its motors, and predicts the energy consumption for trajectories with variable accelerations and variable payloads. The experimental validation of the proposed model was performed with a Nomad Super Scout II mobile robot which was driven along straight and curved trajectories, with different payloads and accelerations. The experiments using the proposed model showed accuracies of 96.67% along straight trajectories and 81.25% along curved trajectories in the estimation of energy consumption.