Abstract: Embodiments of the present disclosure are directed towards a system and method for performing an inspection of an underwater environment. Embodiments may include providing an autonomous underwater vehicle (“AUV”) and performing an inspection of an underwater environment using the AUV. Embodiments may further include acquiring real-time sensor data during the inspection of the underwater environment and applying an active simultaneous localization and mapping (“SLAM”) algorithm during the inspection, wherein applying includes estimating one or more virtual landmarks based upon, at least in part, at least one past measurement and a current estimate of AUV activity.

Abstract: According to an aspect of the invention, a method of optimal safe landing area determination for an aircraft includes accessing a probabilistic safe landing area map that includes a plurality of probabilistic indicators of safe landing areas for the aircraft. A processing subsystem that includes one or more processing resources generates a list of candidate safe landing areas based on the probabilistic safe landing area map and one or more constraints. At least two of the candidate safe landing areas are provided to a path planner. The list of candidate safe landing areas is ranked based on results from the path planner indicating an estimated cost to reach each of the candidate safe landing areas. Based on the ranking, an indicator of an optimal safe landing area is output as a desired landing location for the aircraft.

Abstract: A method for path planning for a plurality of vehicles in a mission space includes determining, with a processor, information indicative of a first local graph of a first vehicle; receiving, with the processor over a communication link, information indicative of a second local graph from a second vehicle; assembling, with the processor, information indicative of a global graph in response to the receiving of the second local graph; wherein the global graph includes information assembled from the first local graph and the second local graph; and wherein the global graph indicates connectivity of objectives for each vehicle of the plurality of vehicles in the mission space.

Abstract: An aspect includes space partitioning for vehicle motion planning. A plurality of obstacle data is analyzed to determine a plurality of obstacle locations in a configuration space of a vehicle. A partitioning of the configuration space is performed to compute a skeletal partition representing a plurality of obstacle boundaries based on the obstacle locations. The skeletal partition is used to preferentially place a plurality of samples by a sampling-based motion planner. At least one obstacle-free path is output by the sampling-based motion planner based on the samples.

Abstract: According to an aspect of the invention, a method of optimal safe landing area determination for an aircraft includes accessing a probabilistic safe landing area map that includes a plurality of probabilistic indicators of safe landing areas for the aircraft. A processing subsystem that includes one or more processing resources generates a list of candidate safe landing areas based on the probabilistic safe landing area map and one or more constraints. At least two of the candidate safe landing areas are provided to a path planner. The list of candidate safe landing areas is ranked based on results from the path planner indicating an estimated cost to reach each of the candidate safe landing areas. Based on the ranking, an indicator of an optimal safe landing area is output as a desired landing location for the aircraft.

Abstract: An aspect includes space partitioning for vehicle motion planning. A plurality of obstacle data is analyzed to determine a plurality of obstacle locations in a configuration space of a vehicle. A partitioning of the configuration space is performed to compute a skeletal partition representing a plurality of obstacle boundaries based on the obstacle locations. The skeletal partition is used to preferentially place a plurality of samples by a sampling-based motion planner. At least one obstacle-free path is output by the sampling-based motion planner based on the samples.

Abstract: A method for path planning for a plurality of vehicles in a mission space includes determining, with a processor, information indicative of a first local graph of a first vehicle; receiving, with the processor over a communication link, information indicative of a second local graph from a second vehicle; assembling, with the processor, information indicative of a global graph in response to the receiving of the second local graph; wherein the global graph includes information assembled from the first local graph and the second local graph; and wherein the global graph indicates connectivity of objectives for each vehicle of the plurality of vehicles in the mission space.

Abstract: A method of route planning for a vehicle proceeding from a current location to a destination in a planning space is provided. The method includes generating a destination-rooted tree from global information that provides cost-to-go routing to the destination from multiple locations in the planning space, generating a vehicle-rooted tree using local information from the current location out to a sensing horizon and determining a local destination at the sensing horizon. The local destination corresponds to minimal cost-to-go routing obtained from the destination-rooted tree.

Abstract: A method of route planning for a vehicle proceeding from a current location to a destination in a planning space is provided. The method includes generating a destination-rooted tree from global information that provides cost-to-go routing to the destination from multiple locations in the planning space, generating a vehicle-rooted tree using local information from the current location out to a sensing horizon and determining a local destination at the sensing horizon. The local destination corresponds to minimal cost-to-go routing obtained from the destination-rooted tree.