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Area coverage is a common task for an unmanned ground vehicle (UGV) that requires time and energy to complete. We have developed a novel cost function that can be used to optimally traverse a path that covers a region. The UGV model and cost function are developed theoretically and verified experimentally. Our cost function weights force inputs, area covered and motor efficiency to create an optimal trajectory. This trajectory is constrained to follow a coverage path described in the literature. The path is modified based on the cost function by replacing turn-in-place maneuvers by moving turns. Tradeoffs are presented for three cases: (1) drive motor efficiency is not considered, (2) the motors are most efficient at the maximum velocity, and (3) the motors are most efficient below the maximum velocity. Optimality tradeoffs include the time required to cover the region, and the energy required to complete the trajectory. Experimental results using an iRobot Packbot are presented. [ABSTRACT FROM AUTHOR]

In this paper, we describe a complete system for mission planning and execution for multiple robots in natural terrain. 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 assignment and 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 range finder, and a video-rate stereo system. An updated version of a dynamic path planner, D*, is used for on-line 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. The work reported here includes a number of results not previously published, including the use of a real-time stereo machine and a high-performance laser range finder, and the use of the GRAMMPS planning system. [ABSTRACT FROM AUTHOR]

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. 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 assignment and ordering of the goals, based on the updated paths to the goals. The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system. [ABSTRACT FROM AUTHOR]