## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.4 Multiple Objective  
As a vehicles mission progresses, the need to adapt to new information or to change objectives may be required. Additionally, balancing multiple internal objectives such as path length, endurance, and safety present challenges to the algorithm development and usage for UAS control. The ability of a system to perform mission re-tasking and path re-planning is needed to enable multiple-objective scenario use. Numerous path planning algorithms discussed earlier integrate object avoidance algorithms. Other safety considerations (such as air collision avoidance) would require unique algorithms that are discussed separately. Multiple-objective path planning deals with integrating multiple path-planning algorithms into one framework to enable a vehicle to perform multiple missions, either in a hierarchical fashion or simultaneously. Multiple-scenario response can be due to the need to respond to changing environments, as seen in [PERSON] _et al._[24]. They proposed a hierarchical approach that removed and replaced mission objectives as the mission requirements are changed or canceled. The approach developed an initial path generation for each vehicle and then, as requirements changed, re-allocation of objectives was performed with each UAS receiving a unique tasking and path generation. [PERSON] and [PERSON] [81; 82] defined a solution for vehicles performing tasks of searching for targets while also tracking targets already found with a hybrid heuristic algorithm that combined a greedy randomized adaptive search procedure with simulated annealing (GRASP-SA). The UASs were provided no knowledge of where or how many targets were present in the environment. At each decision point, the UAS was required to determine whether to continue searching for new targets or track the targets already detected. The approach was performed in an urban environment model, incorporating object avoidance and line-of-sight obstructions into the decision process. The GRASP-SA algorithm was successfully applied to the problem set and provides a unique approach to the multiple-objective problem for search and tracking of multiple targets. The use of Tabu search as a method for multiple-objective planning was proposed by [PERSON] [21]. This early evaluation of the problem requires additional work but showed promise as a way to plan missions for multiple-task planning with goals of maximizing number of completed tasks with a minimization of range and time. The Tabu search algorithm is used to optimize the task allocation scheme after a planning model is built. The problem set evaluated was simple, and requires a more complex and practical environment to be evaluated in order to determine the overall value of this approach. An algorithm utilizing multi-criteria decision making cost functions and multi-attribute utility theory to make complex decisions for vehicle path planning was designed by [PERSON] _et al._[83] with a focus on UAS delivery of medical supplies while flying in a complex airspace. The approach focused on flying a vehicle under existing visual flight rules in the national airspace, which continues to be a critical concern. For en-route planning, multiple criteria were considered within the cost construct of the algorithm: time, fuel, airspace classes, aircraft separation risk, storm cell risk, cruising levels, and population risk. These concerns, while more tactically focused on completing a singular mission type (medical delivery), could be transformed into other objectives in a similar construct for more complex efforts. [PERSON] [84] proposed the use of a decentralized control scheme involving multiple vehicles performing a multi-objective trajectory tracking and consensus problem using particle swarm optimization. The approach incorporated a two-level decision process: a high-level supervisory level and a local vehicle control level. Decentralized model predictive control was utilized for the vehicle-level synthesis of cooperative and self behaviors.