Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A*-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving large, dynamic graphs.

Abstract—This paper discusses the development of a multiobjective mission flight planning algorithm for Unmanned Aerial System (UAS) operations within the National Airspace System (NAS). Existing methods for multi-objective planning are largely confined to two dimensional searches and/or acyclic graphs in deterministic environments; many are computationally infeasible for large state spaces. In this paper, a multi-objective fuzzy logic decision maker is used to augment the D * Lite graph search algorithm in finding a near optimal path. This not only enables evaluation and trade-off between multiple objectives when choosing a path in three dimensional space, but also allows for the modelling of data uncertainty. A case study scenario is developed to illustrate the performance of a number of different algorithms. It is shown that a fuzzy multiobjective mission flight planner provides a viable method for embedding human expert knowledge in a computationally feasible algorithm. U

Abstract — In this paper we describe a state lattice based motion planning approach, which we have successfully applied to large, cluttered, but quasi-static environments. Our approach produces smooth and complex maneuvers through the use of a multi-resolution state lattice, where the resolution is adapted based on the environment, and distance from the robot. We also describe a framework for detecting dynamic obstacles such as pedestrians and cars using a multisensor lasercamera detection and tracking method. Image detection is based on several extensions to the Implicit Shape Model technique; laser detection is instead achieved through the use of a Conditional Random Fields reasoning. Objects are tracked through the use of multiple motion model Kalman filters in order to cope with several different motion dynamics. Urban environments, are complex, cluttered, and dynamic scenes, however. We therefore propose to extend our dynamic obstacle detection and tracking method with a short-term motion prediction functionality based on the same models used for tracking, effectively generating time based cost or risk maps. We further propose to implement these cost maps into our high-dimensional (5D to 6D) lattice planner to generate time-optimal trajectories in dynamic, cluttered environments. A D * implementation is envisioned to speed up re-planning dramatically. I.

An urban operation of unmanned aerial vehicles (UAVs) demands a high level of autonomy for tasks presented in a cluttered environment. While fixed-wing UAVs are well suited for long-endurance missions at a high altitude, enabling them to navigate inside an urban area brings another level of challenges. Their inability to hover and low agility in motion cause more difficulties on finding a feasible path to move safely in a compact region, and the limited payload allows only low-grade sensors for state estimation and control. We address the problem of achieving vision-based autonomous navigation for a small fixed-wing in an urban area with contributions to the following several key topics. Firstly, for robust attitude estimation during dynamic maneuvering, we take advantage of the line regularity in an urban scene, which features vertical and horizontal edges of man-made structures. The sensor fusion with gravity-related line segments and gyroscopes in a Kalman filter can provide driftless and realtime attitude for flight stabilization. Secondly, as a prerequisite to sensor fusion, we present