Computing Efficient Trajectories for Unmanned Air Vehicles. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 6, Volume 32, Issue 4, December 2007, Page 1-9 PDF (344.39 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2020.128899 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Jamal Azzam* | ||||
| Electrical Engineering Department., Egypt | ||||
| Abstract | ||||
| Rapidly developments in the design and implementation of autonomous Unmanned Air Vehicles (UAVs) make the use of them in combat missions a real fact in the near future. One of the challenging problems yet to be solved is the real time planning of the optimal trajectory. Techniques used for robot motion planning are implemented for UAV e.g. cell decomposition, road maps and virtual forces. In these techniques a cost function consists of the threat cost and the length cost with priority factors and weighting quantities for both. The optimal trajectory (or timed path) is that producing the minimum cost function. Although this cost functional is composed of the two cost elements (length and threat) it conceals a source of error that produces optimal paths with high threat values i.e. not actually optimal and not safe. These results return to: a) the length cost dominates the effect on the cost function even with large weighting and priority for the threat cost. b) radar threats or probabilities of being detected by adverse radars are only considered, while threats of Surface to Air Missiles (SAMs) are not included in the threat function. c) weighting quantities given may be suitable for costs of some path edges and not suitable for other edges of the same path and other paths. In this paper a proposed algorithm to compute the real efficient path depending upon a proposed fitness function. The function gives a balanced domination for both length fitness and threat fitness. Besides, the threat on it consists of the two components: radars and SAMs. The algorithm autonomously computes the proper values of the weighting quantities that guide the fitness function towards the higher priority cost element in case of time constraint. It also gives the vehicle a necessary flexibility to adjust the pre-given priority factors in order to compute other optimal paths that fulfills a rendezvous time with the other team members. | ||||
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