## 4 Review of Existing Methods and Capabilities
### Safety Controls
#### 4.2.2 Collision Avoidance  
Collision control has been a key concern for all aircraft, but is more difficult on a UAS due to the lack of onboard pilots. The vehicle must be able to avoid collision with the ground, with other vehicles, and any objects it may encounter. Developing algorithms to perform vehicle maneuvering for collision avoidance can be performed by methods previously discussed in Section 4.1. The difficulty in collision avoidance is identification of the risk and determining what mitigation must be performed. The challenge is identifying what sensor or model is required to determine the risk and what mitigations can be implemented. Ground collision avoidance and recovery systems were originally developed for manned aircraft. In 1990 a patent was granted for an \"Aircraft ground collision avoidance and autorecovery systems device\" [108] which provided a system design for calculating aircraft flyups to prevent ground collision. Automatic Ground Collision research efforts [109; 110; 111; 112] continued in the USAF and NASA for years. In early 2015, an Automatic Ground Collision Avoidance System installed on the F-16, which was based upon the earlier research by the USAF and NASA, saved the pilot and aircraft during operations against the Islamic State in Syria [113]. Recently, NASA [114] integrated a similar system on a small UAS with a smart-phone interface for ground collision avoidance capabilities. These systems rely upon an onboard digital terrain elevation model of the earth to calculate threat and recovery, nullifying the need for additional sensors on the vehicle. Avoiding colliding with stationary objects is an additional consideration in the ground collision avoidance area. [PERSON] _et al._[115] proposed implementation of [PERSON]'s equation to develop a potential field solution. [PERSON] [116] utilized a cross-entropy method in conjunction with rapidly expanding random trees for object avoidance path generation. [PERSON] [117] proposed using an expanding elliptical search method to determine paths for object avoidance. Air collision avoidance of both aircraft and stationary objects is a more difficult problem to solve. A patent was filed in 2001 [118] that provided the initial concept of a safety zone sphere around a vehicle that required both passive and active sensors to monitor the safety zone. Once incursion was detected, the system would inform an onboard sense-and-avoid computer for corrective action response. This underlying philosophy has been utilized in numerous research efforts. One of the critical issues for air collision avoidance is awareness of air traffic within the local airspace. For vehicles that do not have sufficient sensors or capabilities, a ground-based sense-and-avoid system (GBSAA) has been proposed for numerous aircraft types [119]. The Department of Defense, under an Army program, is in the process of developing and deploying several GBSAA systems in specific areas around the US [120]. However, there are challenges to using GBSAA for UAS operations, including the potential that they may not be able to actually support collision avoidance between vehicles [121]. The additional disadvantage of a GBSAA system is that it still requires integration with the UAS or have a pilot in control of the vehicle to make avoidance decisions. Maneuver algorithms are rather straightforward to implement depending on the path requirements; however, integration of GBSAA sensor data is more difficult. Most sensors installed on vehicles or the ground are only sensing other vehicles that may threaten the vehicle of concern. The integration of that information along with vehicle path planning and control for collision avoidance is required. One approach for being able to identify where all traffic is for collision avoidance sensing is the requirement by the FAA to have Automatic Dependent Surveillance-Broadcast (ADS-B) on all aircraft by the year 2020 [122]. [PERSON] and [PERSON] [123] proposed a method of collision avoidance utilizing ADS-B with a greedy rapidly exploring random tree.