System and method for decentralized, multi-agent unmanned vehicle navigation and formation control

A method for navigating a fleet of multiple unmanned vehicles from a first starting location to a second ending location via an initial pathway of waypoints, the iterative method for each of the unmanned vehicles comprising:

• accumulating sensor information representative of obstacle locations and boundaries of one or more sensed obstacles and vehicle locations of other sensed vehicles in the fleet between the first and second locations;
• calculating artificial potential field (APF) additive force vectors representative of virtual forces between each of the vehicles and between each vehicle and the sensed obstacles, including calculating when a force vector is zero which is representative of a local minimum;
• responsive to the local minimum, communicating to the fleet that a new path needs to be generated and generating a new path forward, including calculating one or more new path forward waypoints such that the local minimum is removed because of attraction to the new waypoints by calculating additive force vectors to remove the local minimum and attract the vehicle to the new waypoints around the sensed obstacles and the other sensed vehicles to the second location;
• where each vehicle iteratively broadcasts position updates and calculates the APF force vectors and where obstacles are sensed and estimated as a point-cloud, the obstacle point-cloud representation is abstracted on a grid representation of a map, a graph search algorithm is used and the waypoints to the destination location are the output, and the waypoints are applied to a real map.

A method for navigating a fleet of multiple unmanned vehicles from a first starting location to a second ending location via an initial pathway of waypoints, the iterative method for each of the unmanned vehicles comprising:

• accumulating sensor information representative of obstacle locations and boundaries of one or more sensed obstacles and vehicle locations of other sensed vehicles in the fleet between the first and second locations;
• calculating artificial potential field (APF) additive force vectors representative of virtual forces between each of the vehicles and between each vehicle and the sensed obstacles, including calculating when a force vector is zero which is representative of a local minimum;
• responsive to the local minimum, communicating to the fleet that a new path needs to be generated and generating a new path forward, including calculating one or more new path forward waypoints such that the local minimum is removed because of attraction to the new waypoints by calculating additive force vectors to remove the local minimum and attract the vehicle to the new waypoints around the sensed obstacles and the other sensed vehicles to the second location;
• where each vehicle iteratively broadcasts position updates and calculates the APF force vectors and where obstacles are sensed and estimated as a point-cloud, the obstacle point-cloud representation is abstracted on a grid representation of a map, a graph search algorithm is used and the waypoints to the destination location are the output, and the waypoints are applied to a real map, and
• where one of the vehicles in the fleet is designated as the current leader vehicle, where a first group of one or more of the UVs lose communication others of the vehicles over a set period of time such that the first group stops operationally relying upon the others of the vehicles and where the current leader vehicle requests information and calls for an election of a new leader vehicle, and where the new leader vehicle listens and waits for responses and disseminates an analysis and decision.