Occupancy Based Map Searching Using Heterogeneous Teams of Autonomous Vehicles

Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

“…Previous work [23] investigated using adaptive sampling methods to provide quasi-optimal solutions of Eq. (9) over the feasible set B R .…”

“…The first [23] (generating a predictive world estimate) and third [24] (path planning) steps have already been addressed in previous works.…”

“…Others such as Baronov and Baillieul [20] and Hoffman et al [21] have posed the searching mission in an information theoretic framework which provides useful metrics for search effectiveness. Previous work at the University of Washington by Rubio et al [22] investigated searching algorithms in the context of adaptive algorithms and other efforts [23] explored the use of novel optimization techniques to address the search problem.…”

Time constrained randomized path planning using spatial networks

“…Later, Pongpunwattana et al incorporated these ideas into overall mission planning and task management schemes which address an agent's state and timing constraints [4]. Previous work investigated classical convex optimization techniques to generate simple paths from a starting point to a goal location for agents with constrained velocity limits [5].…”

“…For additional material see [6], [7], [8], [9], and [10]. Difficulties with these strategies are: they require extensive computational power (evolutionary algorithms [4], [11], are limited to generating simple paths (convex optimization techniques [5]), and many other open problems. This paper presents a set of computationally efficient algorithms for generating quasi-optimal paths through a complex environment.…”