A computer program automatically generates plans for a group of robotic vehicles (rovers) engaged in geological exploration of terrain. The program rapidly generates multiple command sequences that can be executed simultaneously by the rovers. Starting from a set of high-level goals, the program creates a sequence of commands for each rover while respecting hardware constraints and limitations on resources of each rover and of hardware (e.g., a radio communication terminal) shared by all the rovers. First, a separate model of each rover is loaded into a centralized planning subprogram. The centralized planning software uses the models of the rovers plus an iterative repair algorithm to resolve conflicts posed by demands for resources and by constraints associated with the all the rovers and the shared hardware. During repair, heuristics are used to make planning decisions that will result in solutions that will be better and will be found faster than would otherwise be possible. In particular, techniques from prior solutions of the multiple-traveling-salesmen problem are used as heuristics to generate plans in which the paths taken by the rovers to assigned scientific targets are shorter than they would otherwise be.
This program was written by Tara Estlin, Gregg Rabideau, Steve Chien, and Anthony Barrett of Caltech for NASA’s Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Software category. This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (818) 393-2827. Refer to NPO-35192.
This Brief includes a Technical Support Package (TSP).
Centralized Planning for Multiple Exploratory Robots
(reference NPO-35192) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package from NASA's Jet Propulsion Laboratory (JPL) detailing a software application designed for centralized planning of multiple exploratory robots, specifically rovers. This software aims to automate the command generation process for a group of robotic agents, enhancing operational efficiency and reducing the workload on human operators.
The centralized planning software operates by modeling each rover separately and integrating these models into a centralized planner. It employs an iterative repair algorithm to resolve conflicts related to resources and other constraints, ensuring that the planning process respects the limitations of each rover and shared hardware, such as communication sites. The software generates detailed command sequences from high-level goals, addressing the complexities of coordinating multiple spacecraft and rover operations.
One of the key features of this software is its use of heuristics derived from solutions to the Multiple Traveling Salesmen Problem (MTSP). This approach optimizes the planning process, resulting in shorter traverses for all rovers. The software has demonstrated significant improvements over existing applications, including a 24% reduction in total traverse time and a 52% increase in available science time during tests involving three rovers, which were planned in about one minute.
The document highlights the unique advantages of this software, such as fast generation of command sequences that can be executed in parallel, coordinated resource management, and integrated solutions for optimizing traversals to scientific targets. It emphasizes that no other software with similar capabilities currently exists, making this application a significant advancement in the field of robotic exploration.
The software is currently in beta status and has been developed under NASA funding, with contributions from various JPL personnel. It is intended for use in NASA's Mars Program, JPL Thinking Systems, and military applications, indicating its potential for both scientific and defense-related operations.
Overall, this Technical Support Package outlines a pioneering approach to robotic planning that leverages advanced algorithms and heuristics to enhance the effectiveness of multiple exploratory rovers, paving the way for more efficient and robust space exploration missions.
