CR methods can be evaluated by a combination of several factors which define the airspace environment. In this review, we evaluate methods according to the following ten characteristics: The timescale on which avoidance planning takes place, the type of surveillance, whether control is centralised or distributed, trajectory propagation, predictability assumption, manoeuvre employed for resolution, approach to multi-actor (>2) conflicts, obstacle types, optimisation objective, and method category. These categories are divided between detection and resolution as per Table 1
and Table 2
, respectively. For each category, the possible variations are presented underneath. More detail is provided in the next subsections.
Aircraft surveillance can be defined in terms of whether the aircraft is dependent on external systems, or on its own on-board systems (i.e., independent). Within the former, an additional distinction can be made based on origin of the data: A centralised system receives data from a common station, whereas a distributed processes information from the surrounding traffic.
For centralised dependent surveillance (Figure 1
a), aircraft are equipped with transponders capable of responding to ground interrogation. Ground sensors determine the 2D position of the aircraft, and altitude is provided by the aircraft. In manned aviation, this is done by ATC, and aircraft are expected to cooperate by broadcasting their altitude and identity. Distributed dependent surveillance (Figure 1
b) uses the ADS-B system; aircraft broadcast their position, altitude, identity, and other parameters by means of a data link, without any intervention from ground systems.
Independent surveillance (Figure 1
c) is more commonly referred to as Sense and Avoid and uses on-board non-cooperative systems/sensors. As unmanned aviation has no broadcast standard system, it commonly resorts to this type of surveillance with on-board sensors which detect both static and dynamic obstacles. It is not employed in manned aviation, however, as aircraft are expected to cooperate through the ADS-B system.
2.2. Trajectory Propagation
Future trajectories of an aircraft can be considered based on their current state (i.e., state-based) or their future intent (i.e., intent-based). The former assumes a straight line as continuation from the current state, whereas the latter assumes turns, and changes in heading and speed, based on the future waypoints of the aircraft.
State-based methods assume a straight-line projection of the aircraft’s current position and velocity vector. In comparison, intent informed can be simulated as a series of straight leg segments. Yang [12
] has shown that reducing a non-linear trajectory to a series of straight lines trajectories allows for accurate computation of conflict states at speeds feasible in real-time complex scenarios. A state-based projection is naturally simpler and faster computationally, as intent requires data transmission and heavier computational processing. However, when future trajectory changes of all involved aircraft are not taken into account, false alarms may occur and future losses of separation, resulting from changes in trajectory, may be overlooked. On the other hand, when conflict resolution is put in place, as aircraft diverge from their initially intended trajectory in order to avoid intrusions, new false alarms are also introduced when considering intent. Research performed for singular cases in the past identified the potential of using intent. Multiple works [12
] have used waypoint information to improve a single intruder’s trajectory prediction. Using both state and intent information in high traffic densities was investigated for civil aviation [16
], improving overall safety. The previous works showed, additionally, that adopting rules disallowing pilots from turning into a conflict prevents intrusions resulting from sudden aircraft manoeuvres nearby. Such can help mitigate the need for intent information. In manned aviation, distributed sharing of future trajectory change points (TCPs) can be done through ADS-B. For unmanned aviation, there is still no research on how this could be performed.
2.3. Predictability Assumption
A conflict is found once it is identified that two aircraft will be closer that the minimum required separation at a future point in time. This process thus require an estimation of the future positions of all aircraft, and it differs on whether uncertainties are added to the trajectory propagation. A nominal assumption (Figure 2
a) does not consider uncertainties (i.e., uncoordinated behaviour from other traffic, unknown wind or state variation). A worst-case assumption (Figure 2
b) considers all possible trajectory changes resulting from uncertainties. However, this is impractical in a real-environment, as its complexity results in a heavy computation. Instead, a middle term, a probabilistic assumption (Figure 2
c) is more often employed. In this case, the likelihood of each possible trajectory change is taken into account based on the current position, and maximum turn and climb rates. Whether to act, and how to act, is decided based on the most likely trajectories.
The nominal assumption is often used in favour of simplicity and good computational performance. This assumption is mostly used with shorter look ahead times (i.e., a few minutes), and can be quite accurate in an environment where aircraft have a steady behaviour. However, accuracy is expected to decrease as the model looks further into the future, as multiple small unexpected changes could have accumulated into a significant change in the trajectory. As a result, alarms predicted far into the future are more likely to be unreliable.
Incorporating uncertainties may improve accuracy; as more potential trajectories are considered, the more likely it is that one will resemble the real observed position into the future. However, this is at the cost of more false positive alarms which are detected in the other trajectories that the aircraft could have taken. Adding more future states of neighbouring aircraft also reduces manoeuvring space. The further you look ahead, the larger the uncertainty space is and smaller the manoeuvring is expected to be, which reduces traffic mobility. It may even reach a situation where no conflict resolution manoeuvre is found, as there is no manoeuvre which avoids all conflicts. A probabilistic assumption provides a solution; fewer trajectories are accounted for depending on their likelihood. This likelihood threshold may be decided based on the number of alarms the model can process within a limited amount of time.
Separation management, or control, may be centralised when decisions regarding future trajectory and conflict resolution are computed in a centralised location for multiple aircraft, or distributed when each aircraft is responsible for its own conflict avoidance. Both approaches rely on a communication network to broadcast information such as intent, trajectories, and priorities.
A centralised system is capable of providing a global solution to complex multiple-actor problems. Uncertainty is reduced as each aircraft follows the solution defined by the centralised agent. Centralised methods typically work towards optimizing trajectories; finding non-intersecting trajectories will guarantee separation. These centralised approaches are often computationally heavy, as a result of having to consider several possible manoeuvres for a number of aircraft, and may therefore not be suitable for real-time implementation when this number increases considerably [18
]. Hypothetically, when all information (the traffic situation, but for instance also flight-specific optimisation preferences) is known and there is sufficient processing power, a centralised approach will lead to the most optimal solution. As all trajectories are known, these can be optimized for all involved aircraft. However, in practice, the demands on the availability of information and the speed of information transfer must be taken into consideration. The availability of optimisation-related information is often limited by the willingness of airlines to share it. The prediction horizon tends to be much larger due to the time it takes to generate and communicate a global solution. Computational intensity increases with the traffic density, thus there is a limit on the number of aircraft a centralised approach can operate simultaneously. Additionally, a single processing point is also a single point of failure, resulting in a central failure mode with global consequences, which is absent in distributed systems.
In manned aviation, ATC is the centralised point responsible for guaranteeing safety of all traffic. Air traffic controllers maintain minimum separation between all aircraft in their airspace sector. Naturally, the traffic density allowed in the sector is thus limited by the maximum number of aircraft that controllers are capable of operating simultaneously. One objective of CD&R research is to reduce the constraint on ATC, whether by creating another centralised point capable of computing optimal trajectories for all involved aircraft without human aid, or distributed systems to be introduced into the on-board systems of each aircraft. In particular, the uprising of unmanned aviation applications, where the number of involved aircraft are expected to greatly exceed the number currently operated by ATC [19
], has prompted the exploration of distributed approaches.
A distributed system reallocates the process of separation assurance from a centralised point to the individual aircraft. As each aircraft only takes into account its neighbouring aircraft when avoiding conflicts, each distributed avoidance system is expected to have only a fraction of the computational strain a centralised system would have. Nonetheless, the speed at which an aircraft can make a decision is still limited by the speed at which information from surrounding traffic is received and processed. A crucial disadvantage of a distributed system is the lack of global coordination from surrounding traffic which may impair safety. Without knowledge of the movement of intruders, decentralised solutions cannot guarantee globally optimal solutions when more than two aircraft are involved. Because of this, the efficiency of decentralisation in resolving multi-actor conflicts is often studied and compared to that of centralised systems: Bilimoria [18
] showed that a distributed resolution strategy can successfully solve complex multiple aircraft problems in real time; Durand [20
] tested this with a no-speed variation scenario where only a centralised system was able to find a solution. Finally, the Free Flight concept [3
] also illustrates that, when aircraft are fully responsible for their own separation from other traffic, they are free to decide upon their optimal route (`direct routing’), versus following the route received from a centralised point for safety. Studies for this project concluded that, once ADS-B technology is developed to a higher reliability and performance, a distributed conflict resolution system can safely guarantee airborne separation.
2.5. Method Categories
This review defines five main categories that can be used as a principal classification for almost all currently existing methods. Two main categories are identified within research for centralised approaches: The exact and heuristic categories. Regarding distributed approaches, we identify three main categories: Prescribed, reactive, and explicitly negotiated. These categories classify methods according to how avoidance manoeuvres/trajectories are identified in environments with multiple aircraft, where all involved aircraft are expected to perform conflict avoidance and modify their path in accordance.
In a centralised approach, a single agent is responsible for deciding the avoidance path of all involved aircraft, thus it is known how aircraft will move in the future. During optimization of an aircraft’s trajectory towards separation, it is assumed that intruders will follow the path set by this agent. The selection of trajectories/avoidance manoeuvres can be optimized towards a preference policy, a certain cost, or in other words to minimize a penalty function. The trajectory with the lower cost from a set of limited possibilities is picked. A preference can be made either for performance (e.g., lower fuel/energy consumption, flight path or time optimization) or safety. It may even be considered that crossing the protected zone of another aircraft, over a small period of time, is better than increasing flight path or adopting a significant change in speed. Methods may be classified on whether these are guaranteed to find the global optimum, i.e., exact algorithms, or heuristic algorithms which attempt to yield a good, but not necessarily global optimum solution. A Mixed Integer Linear Programming (MILP) approach is commonly used for finding the global optimum [23
]. However, an exact algorithm needs a high computing time making it usually impractical for applications in real life [24
]; thus heuristic algorithms, although not guaranteeing optimality, are often employed to shorten execution times. Commonly used heuristic approaches are Variable Neighborhood Search (VNS) [25
], Ant Colony optimization [26
], and Evolutionary Algorithms (EA) [27
In both prescribed and reactive categories, coordination between aircraft is implicit. Traffic either reacts in accordance with a pre-defined set of rules (i.e., prescribed) or a common manoeuvre strategy in response to the conflict geometry (i.e., reactive). Prescribed is mainly achieved by application of the Right-of-Way (RoW) [29
] rules. In short, these define that traffic from the left must give-way, overtaking aircraft manoeuvre to the right, and head-on conflicts are resolved with both aircraft turning to the right. However, Balasooriyan [30
] demonstrated that applying these rules results in a higher number of losses of separation and conflicts than employing other rule sets where both aircraft are expected to initiate a trajectory change to avoid conflicts. When both aircraft adopt a deconflicting route, time in conflict decreases as both aircraft are moving away from each other. Reactive methods `react’ to the position of the intruders; avoidance manoeuvres are a direct result of the conflict geometry. A common example is to use the `shortest-way-out’ principle, which assures implicit coordination in one-to-one conflicts, as single conflicts are always geometrically symmetrical [22
]. To be noted the latter and the RoW coordination define rules for conflict pairs. As the minimum separation distance represents the distance between two aircraft, multi-actor conflicts are simultaneous occurrences of two-aircraft conflicts. When implementing a coordination rule per pairwise conflict, it may be that given the geometry of the conflict, an aircraft receives contradicting solutions for solving its multiple pairwise conflicts. For example, when resolving pairwise conflicts sequentially, the avoidance manoeuvre to the closest conflict can aggravate the next pairwise conflict or even create secondary conflicts with other aircraft. Such prompts the study and verification of implicit rules among different multi-actor conflict geometries; research aims to resolve this issue by developing better ways of implicit coordination, combination of avoidance manoeuvres, and/or prioritization [32
Resolution methods in the explicitly negotiated category resolve conflicts based on explicit communication between aircraft. There is no uncertainty regarding intruder movements as these are clearly defined in the shared information. This data sharing towards deconflicting can be done by setting a negotiation mechanism, where aircraft communicate towards an agreement [33
], and/or prioritization in which a lower-priority aircraft follows an avoidance manoeuvre based on the communication from aircraft with more priority. There are advantages for both cases; a negotiation allows aircraft to share/act according to their preferred policy. The objective is for the final solution to be the best globally possible for all. However, in any negotiation there is the risk of a deadlock, where aircraft communicate indefinitely without reaching an agreement. Some sort of prioritization, respected by all involved aircraft, can limit the number of interactions. Priority can be based on factors such as aircraft current speed, proximity to destination, rules of the air (RoTA) [34
], conflict geometry, or even type of operation. In any case, the rate of communication is a crucial factor. The communication frequency of the network is limited in bandwidth and aircraft may be unable to exchange data at a high frequency. Thus, the number of interactions in any case must be limited comparably to a real life scenario. The number of data transmissions necessary to reach an agreement, to establish a priority (when not implicit), or of sequential messages to the next aircraft in a priority sequence, must be optimized according to this limit. Additionally, a break condition must be added to the communication cycle to prevent aircraft from negotiating or waiting for data from other aircraft indefinitely.
Approximately one third of the researched CR methods does not follow either of the previously mentioned categories. For unmanned aviation, this is mainly in cases where only static obstacles are expected, and therefore, there is no uncertainty regarding future behaviour, or in cases when other aircraft do not have a conflict avoidance mechanism and their path is thus not expected to suffer alterations (e.g., Klaus [35
], Teo [36
]). For manned aviation, different approaches include mostly research works focused on airspace structure in order to guarantee minimum separation. Works such as Mao [37
], Treleaven [38
], and Christodoulou [39
], resort to traffic flows which limit the movement of aircraft. These flows are separated by a safe margin and the lateral displacement when aircraft switch to a different flow is coordinated. Finally, other research, such as Bilimoria [18
], Christodoulou [39
], and Lupu [40
], focus predominantly on the effects of different manoeuvres in similar conflict situations.
2.6. Multi-Actor Conflict Resolution
Centralised and distributed systems have different approaches to multi-actor conflicts. The former works towards a joint optimization of all involved trajectories, until a safe distance between all traffic is achieved. In such centralised systems, the number of conflicts and the degree of connection between trajectories will affect the speed with which the system will converge to its solution. It may also occur in complex situations that no solution is found. Centralised approaches may be divided into two main categories: Sequential algorithms which optimize trajectories one by one according to prioritization of aircraft [41
], and concurrent resolution, where all trajectories are computed simultaneously [42
]. The first of these two approaches is less computationally demanding; for each interaction, the system iterates over possible trajectories for a specific aircraft. Once a safe trajectory is found, it moves on to the next aircraft. When a safe trajectory is identified for each aircraft, a solution is found. This approach requires an adequate prioritization order, to be able to guarantee identification of safe trajectories for all involved aircraft [43
]. Concurrent resolution methods do not require prioritization; however, application of such methods is often only possible on the assumption of limited uncertainty, which is required to reduce the complexity of the calculations. Durand [20
] mentions, for example, an assumption of constant speeds and perfect trajectory prediction, or having the manoeuvres start at the same known optimization time step.
For distributed systems, avoidance manoeuvres adopt the point of view of each aircraft and local optimisation is the objective. At higher traffic densities, where conflicting aircraft pairs can no longer be considered as disconnected from other traffic, this local optimization does not guarantee a globally optimal solution, and there is a risk of unwanted emergent behaviour from interactions between multiple aircraft working individually. The resolution capacity of distributed systems is limited to the intruders the aircraft is capable of detecting. The solution to a subset of aircraft can unknowingly lead to future secondary conflicts with other aircraft, creating a chain reaction of conflicts, or in ultimate, very high traffic density cases, infinitely perpetuating chain conflicts, or Brownian motion [45
]. How distributed methods deal with multi-actor conflicts is therefore a key characteristic of these methods. In this paper, we distinguish between three distributed approaches to multi-actor conflicts: Joint solution, pairwise sequential, and pairwise summed. In a joint solution, multiple intruders are considered simultaneously and a single solution is found that simultaneously resolves all conflicts that the ownship is involved in. In order to limit the complexity of a solution, CR models normally detect and resolve within a limited look-ahead time. Other distributed approaches generate pairwise resolutions, focusing only on individual conflict pairs. In pairwise sequential resolution, each manoeuvre resolves a conflict with one intruder, starting with the highest-priority conflict. Other methods, such as Hoekstra [21
], sum the resolution vectors resulting from each pairwise resolution (i.e., pairwise summed). A single manoeuvre is then computed and performed resulting from this sum. The choice of whether to employ a pairwise or joint resolution also has consequences on the method’s ability for implicit coordination. As previously mentioned, for example, the `shortest-way-out’ principle in pairwise conflicts ensures implicit coordination. However, when summing or in a joint solution implicit coordination is not guaranteed. Nevertheless, as shown by Hoekstra [21
], the summing of the avoidance vectors has a beneficial emergent, global effect of distributing the available airspace between the different vehicles.
2.7. Avoidance Planning
The planning of a manoeuvre can be defined as per the look-ahead time and the state of the aircraft after the avoidance manoeuvre is performed: Strategic is a long-range action which changes the flight-path significantly; tactical is a mid-range action that changes a small part of the flight path; escape is a short-term manoeuvre that brings the aircraft to safety with no additional consideration regarding the flight path. Figure 3
illustrates the differences.
A strategic manoeuvre (Figure 4
a) is normally employed with more than 20 min to loss of separation (LoS), and may even extend to a pre-departure action. It affects the planned flight considerably, as future waypoints are modified to avoid conflict. In manned aviation, Air Traffic Control (ATC) is responsible for strategic and tactical avoidance planning. One of the ways to aid air traffic controllers would be to delegate part (or all) of the separation responsibility to the aircraft crew. In manned aircraft, this is made possible by resorting to on-board systems which receive broadcast information from nearby traffic; such system is called Automatic Dependent Surveillance-Broadcast (ADS-B). In comparison, unmanned aviation often employs (independent) sensors to detect other traffic. Given the physical limitations of such means of surveillance, these are tactical systems. A deviation manoeuvre is performed in order to avoid obstacles (Figure 4
b). From all possible manoeuvres which prevent loss of separation, CR methods attempt to identify one which minimizes either distance from the desired path, flight time, or even fuel consumption or energy. The recovery to the initial flight plan is often not included in the tactical plan; normally aircraft will just redirect to the next waypoint after the conflict situation has been resolved.
In manned aviation, CD&R methods are used for loss of separation avoidance. Escape manoeuvres are not usually employed. Given the large minimum separation distance employed in manned aviation, i.e., ICAO’s [47
] definition of 5 NM horizontal separation and 1000 ft vertical separation, a loss of separation does not necessarily represent a collision (see Figure 5
). In cases where a collision is emergent, Traffic alert and Collision Avoidance System (TCAS) and Ground Proximity Warning System (GPWS) are used instead of CD&R. For these systems, pairwise collision avoidance is the only objective. No similar mechanism is currently available for unmanned aviation, and therefore, CD&R must atone for this gap. Moreover, there’s no pre-defined standard separation distance, and considerably small values may be used (e.g., 50 m [48
]). Thus, there is a higher chance that the drone is close to a collision once it has lost minimum separation. As a result, contrary to maned aviation, unmanned aviation research employs escape manoeuvres (Figure 4
c). This, a last resource within seconds prior to collision, solely attempts to escape the obstacle with no additional considerations. Contrary to a tactical manoeuvre, typically no coordination or optimization is employed in these cases due to the lack of time for it.
2.8. Avoidance Manoeuvre
To avoid a future loss of minimum separation, several resolution manoeuvres can be used which will change the initially intended trajectory. These can be based on changing the current state: Heading variation (Figure 6
a), aircraft change their current heading; speed variation (Figure 6
b), which will change the position of the aircraft for a given point in time; vertical variation (Figure 6
c), where aircraft increase or decrease altitude; or an aircraft may change its future intent by changing its flight-plan. One or multiple of these manoeuvres are performed as to follow a conflict free path. Most CR methods are set on decreasing the number of manoeuvres performed, resulting in a minimum deviation from the original path.
Methods are often restricted to manoeuvres on the horizontal plane. Only a small percentage also consider vertical avoidance manoeuvres. There are advantages for both. Adding a degree of freedom allows for a variety of conflict avoidance movements. On the other hand, the extra degree of freedom results in a more complex optimal route calculation. This could be vital given that a solution must be found before loss of minimum separation. TCAS is singular in only applying vertical manoeuvres. For resolving short-term conflicts, climb/descend is a fast and efficient action since the required vertical separation is smaller than the horizontal one. Sunil [49
] showed that for a stratified airspace, having only horizontal resolutions improves stability; less conflicts are considered and accounted for with only an horizontal conflict layer. Not including vertical changes is also acceptable from a performance point of view, as the latter is highly affected by the flight level the aircraft is operating in. Additionally, travelling at high altitudes is not the best scenario for speed manoeuvring: When the stall speed increases, the manoeuvring space decreases.
Initially, most CD&R methods used heading changes as preferred by air traffic controllers, as they often segment the airspace into layers. Lately, speed variation has received new attention with `subliminal’ speed control, which consists of modifying the aircraft speeds within a small range around their original speeds without informing air traffic controllers. As a result, some of the work of air traffic controllers can be automated thus reducing their workload. Research such as the ERASMUS project [50
] and Chaloulos [51
] show that, although for simple two-aircraft situations subliminal control can reduce the workload of air traffic controllers, its efficiency depends on the nominal minimal separation between the aircraft and on the time available to loss of separation. Conflict resolution based on speed change alone is naturally only possible with non-(near-)head-on conflicts. The likelihood of these kind of conflicts is dependent on the airspace structure and the heading difference between aircraft flying at similar flight levels. In other methods, such as Hoekstra [21
], Rey [52
], and Balasooriyan [30
], combining heading and speed deviations showed potential results.
Flight plan modifications change the waypoints the aircraft is intended to follow. This is similar to real-life operations with flight paths being defined through successive waypoints. This way of avoiding conflicts has gained new attention with the development of the four-dimensional trajectory-based operation (4DTBO) concept [53
]. This refers to 3D waypoints associated with timestamps defining when the aircraft is expected to reach each waypoint. With 4DTBO, the complete path and duration of the flight can be defined by specifying arrival times for a sequence of waypoints. Whenever it is detected that the initially defined 4D waypoints for all involved aircraft will result in one or more losses of minimum separation, new flight plans are constructed by either selecting different waypoints, different arrival timestamps, or both.
Performance limitations naturally have an impact on the manoeuvrability of aircraft. When defining a conflict avoidance, maximum turn rates, and maximum speed and accelerations ranges must be taken into account. Defining a heading and/or speed change which the aircraft cannot successfully complete, will jeopardize the success of the manoeuvre on achieving minimum separation from other traffic. Moreover, different look-ahead requirements may be considered based on speed ranges. For unmanned aviation, taking into account performance differences is an especially important factor given the large range of possible missions which can involve many different types of UAVs (e.g., rotorcraft, fixed-wing). To prevent the calculation of avoidance manoeuvres outside performance limits, methods, such as Van Dam [31
], define a solution space bounded by the possible range of speeds; it is not possible to define an avoidance manoeuvre outside of these boundaries. Still, the speed range is often defined per aircraft type without taking the environment into account. As mentioned before, the manoeuvring space is dependent on the altitude of the aircraft. Studies such as Lambregts [54
], attempt to develop a conflict resolution method with an envelope protection functionality which can identify the maximum manoeuvring space in order to take advantage of the full performance capabilities of the UAV.
Finally, avoidance manoeuvres may also be distinguished on whether they are discrete or continuous; on whether an avoidance is calculated given a discrete state and assumes no modification of this state until the manoeuvre is terminated, or if the environment is observed periodically during the manoeuvre which is adapted incrementally in response. In theory, most avoidance algorithms have a discrete implementation, since they calculate resolution manoeuvres that should resolve the conflict without further intervention. However, in practice, these algorithms can still be used to reevaluate conflicts at each update cycle of the implementation. In this case, in each update cycle where the ownship is detected to be in conflict, the conflict avoidance algorithm outputs an avoidance manoeuvre given the current state of the environment. As a result, the ownship may change a previously-defined avoidance manoeuvre at any update step, based on the changed nature of the traffic situation.
2.9. Obstacle Types
A CD&R method may prevent collision only with static obstacles, with dynamic obstacles, or with all (i.e., both static and dynamic obstacles). When a model avoids solely static objects, it may be inferred that it has strategic planning, with the trajectory being set before the beginning of the flight in a known environment.
Manned aviation CD&R models will naturally be directed at detecting other dynamic traffic as these models are mostly used when aircraft are flying at cruise altitude. It should be noted that it is not guaranteed that a model directed at dynamic obstacles can also avoid static obstacles. First, while most of these CD&R models assume obstacles as a circle with radius equal to the minimum separation distance, a static object can have different sizes. Second, most models also assume some sort of coordination and non-zero speed. Most dynamic obstacle oriented CD&R models would have to be enhanced when transposed to, for example, an urban environment where deviation also from static objects, such as buildings, must be guaranteed.
For unmanned aviation, a considerable number of CD&R models still focus solely on static obstacles. However, these can only be used for operations where the environment is well known in advance. This is the case of, for example, an area where a drone must carry an object from a start to an end point, and no other traffic is expected.
For CD&R methods, safety is paramount; however, there is a preference for methods which do not significantly alter the initially planned trajectory or heavily increase the costs of an operation. The efficiency of a CR method can be evaluated regarding its effect on the time and/or path of the flight or even fuel/energy consumption. To be noted that a CR method may contain weights of costs which vary based on the mission/situation, thus its efficiency being dependent not only on the intrinsic method but on the weights employed.
A simple way to minimise path length is to be partial to small heading changes when avoiding obstacles [37
]. Minimizing flight time can be a direct consequence of minimizing flight path when the speed is assumed constant. In other cases, minimizing flight time results in a preference for avoidance manoeuvres which do not include lowering the aircraft’s speed.
Computing fuel expenditure is not direct, as it depends on several physical factors of the aircraft such as model, speed, and weight at the moment of the operation. A simplification is to opt for the manoeuvre which minimizes speed variation [23
] as the latter is a major coefficient on fuel waste. From the examined research, the Base of Aircraft Data (BADA) performance model [55
] is preferred for fuel consumption calculations [52
]. For unmanned aviation, energy efficiency based CD&R is currently being research as more drones are developed and more information on theses systems is made available. Research, such as Dietrich [56
] and Stolaroff [57
], offer a first look at estimating drones’ energy consumption.