  Many overall safety factors need to be considered in the next generation of
Urban Air Mobility (UAM) systems and addressing these can become the anchor
point for such technology to reach consent for worldwide application. On the
other hand, fulfilling the safety requirements from an exponential increase of
prolific UAM systems, is extremely complicated, and requires careful
consideration of a variety of issues. One of the key goals of these Unmanned
Air Systems (UAS) is the requirement to support the launch and control of
hundreds of thousands of these advanced drones in the air simultaneously. Given
the impracticalities of training the corresponding number of expert pilots,
achieving this goal can only be realized through safe operation in either
fullautonomous or semi-autonomous modes. According to many recent studies, the
majority of flight accidents are concentrated on the last three stages of a
flight trip, which include the Initial Approach, Final Approach, and Landing
Phases of an airplane trip. Therefore, this paper proposes a novel
decentralized processing system for enhancing the safety factors during the
critical phases of Vertical and/or Short Take-Off and Landing (V/STOL) drones.
This has been achieved by adopting several processing and control algorithms
such as an Open Fuzzy Logic System (FLS) integrated with a Flight Rules Unit
(FRU), FIR filters, and a novel Prognostic Malfunction processing unit. After
applying several optimization techniques, this novel coarse-grained Autonomous
Landing Guidance Assistance System (ALGAS3) processing architecture has been
optimized to achieve a maximum computational processing performance of 70.82
Giga Operations per Second (GOPS). Also, the proposed ALGAS3 system shows an
ultra-low dynamic thermal power dissipation (I/O and core) of 145.4 mW which is
ideal for mobile avionic systems using INTEL 5CGXFC9D6F27C7 FPGA chip.
