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Barry Brumitt

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
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Possible papers

4

ICRA Conference 1998 Conference Paper

Experiments in Autonomous Driving with Concurrent Goals and Multiple Vehicles

  • Barry Brumitt
  • Martial Hebert

In this paper we report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e. g. , the ordering of the goals, based on the updated paths to the goals. Two recently developed sensors are used for obstacle detection: a high-speed laser rangefinder and a video-rate stereo system. An updated version of a dynamic path planner D* is used for online computation of routes. A new mission planning and execution monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles. We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals. This system is the first multivehicle and multigoal system to be demonstrated in real, natural environments with this degree of generality. The work reported here includes a number of results not previously published, including the use of a real-time stereo machine, a high performance laser rangefinder and the GRAMMPS planning system.

ICRA Conference 1998 Conference Paper

Framed-Quadtree Path Planning for Mobile Robots Operating in Sparse Environments

  • Alex Yahja
  • Anthony Stentz
  • Sanjiv Singh
  • Barry Brumitt

Mobile robots operating in vast outdoor unstructured environments often only have incomplete maps and must deal with new objects found during traversal. Path planning in such sparsely occupied regions must be incremental to accommodate new information, and, must use efficient representations. In previous work we have developed an optimal method D* to plan paths when the environment is not known ahead of time, but, rather is discovered as the robot moves around. To date, D* has been applied to a uniform grid representation for obstacles and free space. In this paper we propose the use of D* with framed quadtrees to improve the efficiency of planning paths in sparse environments. The new system has been tested in simulation as well on an autonomous jeep, equipped with local obstacle avoidance capabilities. We show how the use of framed quadtrees improves performance in terms of path length, computation speed, and memory requirements.

ICRA Conference 1998 Conference Paper

GRAMMPS: A Generalized Mission Planner for Multiple Mobile Robots in Unstructured Environments

  • Barry Brumitt
  • Anthony Stentz

For a system of cooperative mobile robots to be effective in real-world applications it must be able to efficiently execute a wide class of complex tasks in potentially unknown and unstructured environments. Previous research in multi-robot systems has either been limited to relatively structured domains or to small classes of feasible missions. This paper describes a field-capable system called GRAMMPS which addresses this problem by coupling a general-purpose interpreted grammar for task definition with dynamic planning techniques. GRAMMPS supports a general class of local navigation systems and heterogeneous groups of robots, providing optimal execution of missions given current world knowledge. Simulations illustrating the capabilities of this system are provided. Results showing successful runs of this system on two autonomous off-road vehicles are also given.

ICRA Conference 1996 Conference Paper

Dynamic mission planning for multiple mobile robots

  • Barry Brumitt
  • Anthony Stentz

Planning for multiple mobile robots in dynamic environments involves determining the optimal path each robot should follow to accomplish the goals of the mission, given the current knowledge available about the world. As knowledge increases or improves, the planning system should dynamically reassign robots to goals in order to continually minimize the time to complete the mission. In this paper, an example problem in this domain is explored and performance results of such a dynamic planning system are presented. The system was able to dynamically optimize the motion of 3 robots toward 6 goals in real time, improving the average overall mission performance compared to a static planner by 25%. A preliminary design for a practical solution to a wider class of problems is also discussed.

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