The use of autonomous aerial vehicles (UAVs) to explore unknown environments is a growing field of research; of particular interest is locating a target that emits a signal within an unknown environment. Several physical processes produce scalar signals that attenuate with distance from their source, such as chemical, biological, electromagnetic, thermal, and radar signals. The natural decay of the signal with increasing distance enables a gradient ascent method to be used to navigate toward the target. The UAVs navigate around obstacles whose positions are initially unknown; a hybrid controller comprised of overlapping control modes enables robust obstacle avoidance in the presence of exogenous inputs by precluding topological obstructions. Limitations of a distributed gradient augmentation approach to obstacle avoidance are discussed, and an alternative algorithm is presented which retains the robustness of the hybrid control while leveraging local obstacle position information to improve non-collision reliability. A heterogeneous swarm of multirotors demonstrates the target locating problem, sharing information over a multicast wireless private network in a fully distributed manner to form an estimate of the signal's gradient, informing the direction of travel toward the target. The UAVs navigate around obstacles, showcasing both algorithms developed for obstacle avoidance. Each UAV performs its own target seeking and obstacle avoidance calculations in a distributed architecture, receiving position data from an OptiTrack motion capture system, illustrating the applicability of the control law to real world challenges (e.g., unsynchronized clocks among different UAVs, limited computational power, and communication latency). Experimental and theoretical results are compared. / Master of Science / In this project, a new method for locating a target using a swarm of unmanned drones in an unknown environment is developed and demonstrated. The drones measure a signal such as a beacon that is being emitted by the target of interest, sharing their measurement information with the other drones in the swarm. The magnitude of the signal increases as the drones move toward the target, allowing the drones to estimate the direction to the target by comparing their measurements with the measurements collected by other drones. While seeking the target in this manner, the drones detect obstacles that they need to avoid. An issue that arises in obstacle avoidance is that drones can get stuck in front of an obstacle if they are unable to decide which direction to travel; in this work, the decision process is managed by combining two control modes that correspond to the two direction options available, using a robust switching algorithm to select which mode to use for each obstacle. This work extends the approach used in literature to include multiple obstacles and allow obstacles to be detected dynamically, enabling the drones to navigate through an unknown environment as they locate the target. The algorithms are demonstrated on unmanned drones in the VT SpaceDrones test facility, illustrating the capabilities and effectiveness of the methods presented in a series of scenarios.