Agent route planning in complex terrains

For many autonomous agents, such as mobile robots, autonomous vehicles and Computer Generated Forces, route planning in complex terrain is a critical task, as many of the agent’s higher-level goals can only be accomplished if the agent is in the right place at the right time. The route planning problem is often formulated as one of finding a minimum-cost route between two locations in a digitised map which represents a complex terrain of variable altitude, where the cost of a route is an indication of its quality. However route planners which attempt to optimise a single measure of plan quality are difficult to integrate into the architecture of an agent, and the composite cost functions on which they are based are difficult to devise or justify. In this paper, we present a new approach to route planning in complex terrains based on a novel constraint-based search procedure, A with bounded costs (ABC), which generalises the single criterion optimisation problem solved by conventional route planners and describe how a planner based on this approach has been integrated into the architecture of a simple agent. This approach provides a means of more clearly specifying agent tasks and more precisely evaluating the resulting plans as a basis for action.