In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL2.1. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the handling of continuous linear change, and the handling of duration-dependent effects in combination with duration inequalities, both of which require tightly coupled temporal and numeric reasoning during planning. COLIN combines FF-style forward chaining search, with the use of a Linear Program (LP) to check the consistency of the interacting temporal and numeric constraints at each state. The LP is used to compute bounds on the values of variables in each state, reducing the range of actions that need to be considered for application. In addition, we develop an extension of the Temporal Relaxed Planning Graph heuristic of CRIKEY3, to support reasoning directly with continuous change. We extend the range of task variables considered to be suitable candidates for specifying the gradient of the continuous numeric change effected by an action. Finally, we explore the potential for employing mixed integer programming as a tool for optimising the timestamps of the actions in the plan, once a solution has been found. To support this, we further contribute a selection of extended benchmark domains that include continuous numeric effects. We present results for COLIN that demonstrate its scalability on a range of benchmarks, and compare to existing state-of-the-art planners.

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COLIN: planning with continuous linear numeric change

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.

/pdf/constraint-satisfaction-techniques-in-planning-and-y5gg732ki5.pdf

Constraint satisfaction techniques in planning and scheduling

Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

https://arc.aiaa.org/doi/pdf/10.2514/6.2012-1275459

A Generalized Timeline Representation, Services, and Interface for Automating Space Mission Operations

Generating and scheduling activities is particularly challenging when considering both consumptive resources and complex resource interactions such as time-dependent resource usage We present three methods of determining valid temporal placement intervals for an activity in a temporally grounded plan in the presence of such constraints We introduce the Max Duration and Probe algorithms which are sound, but incomplete, and the Linear algorithm which is sound and complete for linear rate resource consumption We apply these techniques to the problem of scheduling awake and asleep episodes for a planetary rover where the awake durations are affected by scheduled activities We demonstrate how the Probe algorithm performs competitively with the Linear algorithm given an advantageous problem space and well-defined heuristics We show that the Probe and Linear algorithms outperform the Max Duration algorithm empirically We then present the runtime differences between the three algorithms The Probe algorithm is currently base-lined for use in the onboard scheduler for NASA's next planetary rover, the Mars 2020 rover

/pdf/scheduling-with-complex-consumptive-resources-for-a-kqt13du84b.pdf

Scheduling with Complex Consumptive Resources for a Planetary Rover

Mission planning is central to space mission operations, and has benefited from advances in model-based planning software. Constraints arise from many sources, including simulators and engineering specification documents, and ensuring that constraints are correctly represented in the planner is a challenge. As mission constraints evolve, planning domain modelers need help with modeling constraints efficiently using the available source data, catching errors quickly, and correcting the model. This paper describes the current state of the practice in designing model-based mission planning tools, the challenges facing model developers, and a proposed Interactive Model Development Environment (IMDE) to configure mission planning systems. We describe current and future technology developments that can be integrated into an IMDE.

https://ti.arc.nasa.gov/publications/3293/download/

The Challenge of Configuring Model-Based Space Mission Planners

Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans. SACE further provides flight controllers with real-time situational awareness and what-if analysis capabilities. SACE is built on the Extensible Universal Remote Operations Planning Architecture (EUROPA) model-based planning system. EUROPA facilitated SACE development by providing model-based planning, built-in constraint reasoning capability, and extensibility. This article formulates the planning problem, explains how EUROPA solves the problem, and provides performance statistics from several planning scenarios. SACE reduces a highly manual process that takes weeks to an automated process that takes tens of minutes.

Matthew E. Boyce

Papers

Planning solar array operations on the international space station