| dc.description.abstract | Autonomy is an essential feature of any robotic system. Aerial robots, commonly known as Unmanned Aerial Vehicles (UAVs), are being integrated into airspace and various trials towards achieving higher levels of autonomy are in progress. When multiple UAVs share the same airspace, safety from inter-UAV conflict is of utmost importance. Collision avoidance is an unavoidable feature of any UAV, and diverse methods addressing this problem are available in the literature.
This thesis presents avoidance maps, a collision avoidance algorithm for fixed-wing UAVs. Avoidance of fixed-wing UAVs is challenging because of their inability to hover in contrast to their rotary-wing counterpart. Further, physical constraints like minimum turn radius make the process less flexible. The proposed avoidance map partitions the control input space of the UAVs into those leading to collision (red region) and avoidance (green region). Here, the control input used is constant lateral acceleration. Various versions of this are developed, which improves its computational cost. The algorithm could be implemented for cooperative, non-cooperative, and multiple UAVs and is demonstrated by suitable examples.
In the next part of this thesis, precision UAV collision avoidance is discussed. This method is characterized by a gradual reduction of applied lateral acceleration during the avoidance process. Precision-control based avoidance optimizes the energy expenditure of the UAVs. The UAVs get away from their initial course while maneuvering. They are brought back to the initial direction of motion using Dubins curves, which joins two points via the shortest distance. The return to the course is achieved by Dubins path, where the necessary maneuvers are chosen from the avoidance map. An avoidance map can be used for realistic systems also. This utility is demonstrated by simulations using guidance models and six-degree-of-freedom UAV models.
The avoidance map is further extended to few versions in the subsequent chapter. A time-graded version is introduced first, which classifies the collision region based on time to collision. This enables the use of several maneuvers from the collision region of the map as well. Next, asynchronous avoidance is introduced, which makes the avoidance process flexible for UAVs. The asynchronous avoidance maps compute avoidance maneuvers with a predetermined time delay for either of the UAVs. This results in one of the UAVs remaining on course for the desired time delay before maneuvering to avoid. Avoidance map is extended for constrained environments like corridors or geo-fences where the control input is the UAV heading angle. The application of avoidance maps for virtual intersections and lane changing for UAV virtual skyways are also discussed in this work.
The last part of the thesis formulates collision avoidance of UAVs using game theory. This applies to both fixed-wing and rotor-craft categories and is based on the solution concept of correlated equilibrium. UAVs are considered to be intelligent players and the conflict resolution process is formulated as a game. The decision-making framework, which is termed CONCORD, works independently of the kind of avoidance algorithm used. The framework is found suitable for cooperative, non-cooperative, and multiple UAVs. It is shown that the proposed framework fairly resolves conflicts among UAVs and guarantees safety. A brief discussion on UAV integration to airspace and concord integration to such UAV traffic management system concludes this work. | en_US |
