Roboti za razminiranje – zahtjevi i ograničenja

One of the most urgently needed applications for mobile robots is demining. Using robots in a minefield is accomplished with severe demands for mobility in an environment covered with dense vegetation and containing various obstacles. Furthermore, it is required that robot should cover the whole area with the detector, avoiding previously detected mines. Different configurations of demining robots are analyzed regarding control and navigation, size and locomotion.Razminiranje je jedno od najvažnijih potencijalnih područja primjene mobilnih robota. Korištenje robota u minskom polju povezano je sa strogim zahtjevima na pokretljivost u okolišu prekrivenom gustom vegetacijom koji sadrži različite prepreke. Povrh toga, robot mora omogućiti pregled cijelog područja detektorom, izbjegavajući prethodno otkrivene mine. U radu su analizirane različite strukture robota za razminiranje s obzirom na upravljanje, navigaciju, veličinu i način kretanja

Scouting algorithms for field robots using triangular mesh maps

Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method

Machining-based coverage path planning for automated structural inspection

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets

Cooperative Unmanned Air and Ground Vehicles for Landmine Detection

The unmanned aerial vehicle used in this research is multi-functional quadcopter with infrared camera and Ground Penetrating Radar (GPR). The unmanned aerial vehicle detects the landmines using infrared camera and GPR; maps a pin in digital map for future use by ground vehicle. The ground vehicle used in this research is Belarus132N mobile robot. It has the following onboard sensors: stereo pair camera, GPS, and image processing system. The ground vehicle will use onboard sensors: stereo pair camera, GPS and the map provided by the quadcopter to traverse the region, and locate the mapped landmines. The base station consists of a laptop that provides a communication link between the aerial and ground vehicle systems and for saving information from any destruction. This proposed system will demonstrate how an air-ground vehicle system use to cooperatively detect, locate and traverse of landmines