Abstract-Operating micro aerial vehicles (MAVs) outside of the bounds of a rigidly controlled lab environment, specifically one that is unstructured and contains unknown obstacles, poses a number of challenges. One of these challenges is that of quickly determining an optimal (or nearly so) path from the MAVs current position to a designated goal state. Past work in this area using full-size unmanned aerial vehicles (UAVs) has predominantly been performed in benign environments. However, due to their small size, MAVs are capable of operating in indoor environments which are more cluttered. This requires planners to account for the vehicle heading in addition to its spatial position in order to successfully navigate. In addition, due to the short flight times of MAVs along with the inherent hazards of operating in close proximity to obstacles, we desire the trajectories to be as cost-optimal as possible. Our approach uses an anytime planner based on A that performs a graph search on a four-dimensional (4-D) (x,y,z,heading) lattice. This allows for the generation of close-to-optimal trajectories based on a set of precomputed motion primitives along with the capability to provide trajectories in real-time allowing for onthe-fly re-planning as new sensor data is received. We also account for arbitrary vehicle shapes, permitting the use of a noncircular footprint during the planning process. By not using the overly conservative circumscribed circle for collision checking, we are capable of successfully finding optimal paths through cluttered environments including those with narrow hallways. Analytically, we show that our planner provides bounds on the sub-optimality of the solution it finds. Experimentally, we show that the planner can operate in real-time in both a simulated and real-world cluttered environments.
Abstract-Planning with kinodynamic constraints is often required for mobile robots operating in cluttered, complex environments. A common approach is to use a two-dimensional (2-D) global planner for long range planning, and a short range higher dimensional planner or controller capable of satisfying all of the constraints on motion. However, this approach is incomplete and can result in oscillations and the inability to find a path to the goal. In this paper we present an approach to solving this problem by combining the global and local path planning problem into a single search using a combined 2-D and higher dimensional state-space.
Abstract-With the continued improvements in portable computing power and sensing systems it is becoming more common for groups of robots to cooperate to achieve a goal. When the robots are operating in an initially unknown environment, the most natural form of cooperation is multi-robot exploration. For many years frontier based approaches have been commonly used to assign target points for each of the robots in the group based on expected information gain and distance to travel. In this paper we present an expansion to these approaches allowing for the incorporation of multiple objective utility functions that allow adjustment of the exploration priorities both for the individual robots and the group as a whole. In addition, we discuss real world results of our algorithm including our first place finish at the Old Ram Shed Challenge and second place at the MAGIC2010 main competition.
In this report, we describe the technical approach and algorithms that have been used by the University of Pennsylvania in the MAGIC 2010 competition. We have constructed and deployed a multi‐vehicle robot team, consisting of intelligent sensor and disrupter unmanned ground vehicles that can survey, map, recognize, and respond to threats in a dynamic urban environment with minimal human guidance. The custom hardware systems consist of robust and complementary sensors, integrated electronics, computation, and highly capable propulsion and actuation. The mapping, navigation, and planning software is organized hierarchically, allowing autonomous decisions to be made by the robots while enabling human operators to interact with the robot team in an efficient and strategic manner. The ground control station integrates information coming from the robots as well as metadata feeds to focus the attention of the operators and respond rapidly to emerging threats. These systems were developed and tested by the UPenn team to complete two phases of the MAGIC 2010 challenge in a safe and timely manner. © 2012 Wiley Periodicals, Inc.
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