Recent Developments in Path Planning for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have demonstrated their effectiveness in performing diverse missions at significantly lower costs compared to the human beings. UAVs have the capabilities to reach and execute mission in those areas that are very difficult for humans to even reach such as forest, deserts, and mines. Integration of the latest technologies including reactive controls, sense and avoid, and onboard computations have strengthened their dominance further in various practical missions. Besides the innovative applications, the use of UAVs imposes several challenges, and one of those challenges is computing a low-cost path for aerial mission by avoiding obstacles as well as satisfying certain performance objectives (a.k.a path planning (PP)). To this end, this chapter provides a concise overview of various aspects concerning to PP including basics introduction of the subject matter, categorization of the PP approaches and problems, taxonomy of the essential components of the PP, performance objectives of the PP approaches, recent algorithms that have been proposed for PP in known and unknown environments, and future prospects of research in this area considering the emerging technologies. With this chapter, we aim to provide sufficient knowledge about one of the essential components of robotics technology (i.e., navigation) for researchers.

An ant colony algorithm model for UAV sense and avoid based on ADS-B

In the context of airspace fusion, in order to improve the safety performance of UAV and prevent the occurrence of air collision accidents, an ant colony algorithm model for UAV sense and avoid based on ADS-B monitoring technology is proposed. The model mainly consists of two parts: the deterministic conflict detection model makes the full use of ADS-B information to calculate the geometric distance from the horizontal and vertical planes to identify the conflict target, and the conflict resolution model is based on the ant colony algorithm which introduces the comprehensive heuristic function and sorting mechanism to plan the route again for achieving the collision avoidance. The simulation results show that the conflict detection model can effectively identify the possible threat targets, and the conflict resolution model is not only suitable for the typical two aircraft conflict scenarios, but also can provide a better resolution strategy for the complex multiple aircraft conflict scenarios.

Rancang Bangun Sensor Radar Sense And Avoid Uav Untuk Smart System Teletransport Alat Kesehatan

<p class="Abstrak">Perancangan sensor radar untuk <em> sense and avoid </em>(SAA) sistem pesawat tanpa awak (UAV) bertujuan agar operasi teletransport alat kesehatan dengan UAV VTOL berjalan dengan aman terhindar dari kecelakaan tabrakan di udara. Sensor radar ini didesain dengan bahan duroid 5880 dengan dielektrik konstant 2.2 dan ketebalan subtrate 1.57 mm. Bentuk antenna circular dan bekerja di pita ku-band 14 Ghz. Terdapat dua sensor untuk Tx dan Rx dalam satu substrate. Hasil simulasi sensor menunjukkan bandwitdh yang lebar 1.5 GHz sehingga mampu menghasilkan resolusi range sangat baik yaitu 9.2 cm. Penguatan antenna dihasilkan 7.32 dB dan sudut beamwidth sensor 83^O arah azimuth dan 78.2^Oarah elevasi. Sensor ini akan disematkan pada sistem SAA dengan algoritma neural network yang mendrive manuever UAV VTOL berbelok kesamping pada sudut dan jarak yang tepat sehingga terhindar dari tabrakan dengan objek penghalang.</p><p class="Abstrak"><strong><br /></strong></p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The design of the radar sensor for the sense and avoid (SAA) system of unmanned aircraft (UAV) aims to make teletransport operations of medical devices with UAV VTOL run safely avoiding collisions in the air. This radar sensor is designed with duroid 5880 material with a dielectric constant of 2.2 and a subtrate thickness of 1.57 mm. The antenna is circular and works on the 14 Ghz ku-band band. There are two sensors for Tx and Rx in one substrate. The sensor simulation results show a wide bandwidth of 1.5 GHz so that it is able to produce a very good range resolution of 9.2 cm. The antenna gain was 7.32 dB and the beamwidth angle of the sensor was 83^O in the azimuth direction and 78.2^O in the elevation direction. This sensor will be embedded in the SAA system with a neural network algorithm that drives the UAV VTOL maneuver to turn sideways at the right angle and distance so that it avoids collisions with obstructions.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>