Automated Rover Sequence Generation (ARSG) is a prototype computer program for ground-based automatic generation of sequences of commands that can be used for a robotic exploratory vehicle (rover) on Mars. ARSG is based on the Automated Scheduling and Planning Environment (ASPEN) computer program, which has been described in several NASA Tech Briefs articles in recent years. Given high-level scientific and engineering activities required of a rover, ARSG automatically generates a sequence of commands that can be executed by the rover within resource constraints and in compliance with flight rules. An automated- planning-and-scheduling software subsystem encodes rover design knowledge and uses search and reasoning techniques to automatically generate low-level command sequences while (1) respecting rover operability constraints, scientific and engineering preferences, environmental predictions, and (2) adhering to hard temporal constraints. By enabling goal-driven command of planetary rovers, this software can reduce the need for highly skilled rover engineering personnel, thereby reducing the costs of mission operations. ARSG enables faster responses to changes in the state of a rover (e.g., malfunctions) or to scientific discoveries by eliminating the time-consuming manual validation of command sequences and enabling rapid “what-if” analyses.
Contributors to this software include Robert Sherwood, Tara Estlin, Darren Mutz, Gregg Rabideau, Steve Chien, Paul Backes, Jeff Norris, Brian Cooper, and Scott Maxwell of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393- 3425. Refer to NPO-30204.
This Brief includes a Technical Support Package (TSP).
Software Generates Sequences of Operations for a Mars Rover
(reference NPO-30204) is currently available for download from the TSP library.
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Overview
The document discusses advancements in automated planning and sequence generation for Mars rovers, focusing on the challenges and solutions associated with current manual processes. Communication delays over interplanetary distances hinder real-time control of rovers, necessitating a rapid cycle of telemetry receipt, analysis, command generation, and uplink, typically within twelve hours. The existing manual methods for generating rover activity sequences are labor-intensive and prone to errors, leading to operator fatigue and reduced scientific output.
To address these issues, the document highlights the need for automated planning tools that can facilitate collaboration between science and engineering teams, optimizing mission planning and execution. Current tools like the Rover Control Workstation (RCW) and the Web Interface for Telescience (WITS) assist in manual planning but lack the capability for automated goal-based planning. The document emphasizes the potential benefits of an automated planning tool, which would allow for faster turnaround times and more efficient use of resources.
The CASPER (Continuous Activity Scheduling, Planning, Execution, and Re-planning) system is introduced as a dynamic onboard planning solution that can generate and modify rover command sequences in real-time, responding to changing operational contexts. CASPER integrates with path planners to navigate obstacles and continuously updates plans based on sensor feedback, allowing for quick adjustments in response to unexpected events, such as equipment failures or changes in power consumption.
The document also summarizes the status of automated commanding across various rover missions, indicating that while some models have been tested, there is a clear need for further development and integration of automated systems. The goal is to reduce the manual workload on engineering teams, enhance the efficiency of science target identification, and ultimately improve the scientific return from rover missions.
In conclusion, the document advocates for the development of automated planning tools to streamline rover operations, reduce human error, and facilitate better collaboration between science and engineering teams. By leveraging AI planning and scheduling technologies, future missions can achieve more efficient and effective exploration of Mars.
