This work focuses on the development and implementation of an autonomous path planning and obstacle avoidance algorithm for an autonomous surface vehicle (ASV) in a riverine environment. The algorithm effectively handles trap situations, which occur when the river bends away from the destination. In addition, the algorithm uses real-time sensor feedback to avoid obstacles. A general global route is proposed based on an a priori shoreline map. Then, local paths are calculated considering both the a priori data and measurements received from an obstacle sensor. These paths roughly follow the global path. The algorithm was tested on an ASV equipped with basic navigational sensors and an omnidirectional camera for obstacle detection, and experimentation verified its effectiveness. / Master of Science