ROSPlan: Planning in the Robot Operating System

A High-Altitude Pseudo-Satellite (HAPS) is a fixed-wing, solar-powered Unmanned Aerial Vehicle (UAV) developed to become a flexible alternative to satellites with fixed-orbits for monitoring ground activities over long periods of time. However, given its lightweight build and weak electro-motors, the platform is rather sensitive to weather and cannot fly around hazardous weather zones swiftly. In this work, we formulate the problem of planning missions for multiple HAPS as a hybrid planning problem expressed in PDDL+. The formulation also considers the problem of modeling the platform dynamics, the time-varying environment, and the heterogeneous tasks that need to be carried out. Additionally, we propose a framework that combines a PDDL+ automated planner with an Adaptive Large Neighborhood Search (ALNS) approach, developed to couple the automated planner with a meta-heuristic that is specific for the problem. The task and motion planning are done in an intertwined way within the framework, preserving hence a common decision/search space. We validate our approach with a third-party realistic simulator for HAPS, as well as with a set of benchmark tests, showing that our integrated approach produces executable mission plans.

Section: Discussionmentioning

confidence: 99%

An AI-Based Planning Framework for HAPS in a Time-Varying Environment

Kiam

Scala

Javega

et al. 2020

“…We also added the explicit notion of rejection and outcome, which is particularly important to enable, e.g., a generate-and-filter approach, which generates a number of candidate goals, and then sifts through them selecting the most relevant one(s) only avoiding conflicts. ROSPlan ROSPlan (Cashmore et al 2015) is a framework for task planning that describes a number of exchangeable components and a set of message types to interconnect these. Such components are, e.g., planner integration (problem generation, invocation, result parsing), and fact base storage.…”

Section: Related Workmentioning

confidence: 99%

Goal Reasoning in the CLIPS Executive for Integrated Planning and Execution

Niemueller

Hofmann

Lakemeyer

2019

The close integration of planning and execution is a challenging problem. Key questions are how to organize and explicitly represent the program flow to enable reasoning about it, how to dynamically create goals from run-time information and decide on-line which to pursue, and how to unify representations used during planning and execution.In this work, we present an integrated system that uses a goal reasoning model which represents this flow and supports dynamic goal generation. With an explicit world model representation, it enables reasoning about the current state of the world, the progress of the execution flow, and what goals should be pursued – or postponed or abandoned. Our executive implements a specific goal lifecycle with compound goal types that combine sub-goals by conjunctions, disjunctions, concurrency, or that impose temporal constraints.Goals also provide a frame of reference for execution monitoring. The current system can utilize PDDL as the underlying modeling language with extensions to aid execution, and it contains well-defined extension points for domain-specific code. It has been used successfully in several scenarios.

“…We have based our evaluation in actual games on the publicly available cluster setup 11 of the Planning and Execution (Cashmore et al 2015), and the winning team from Freiburg. 14 For each team combination, we have run 100 games, resulting in a total of 600 games.…”

Section: Simulated Tournamentmentioning

confidence: 99%

ASP-Based Time-Bounded Planning for Logistics Robots

Schäpers

Niemueller

Lakemeyer

et al. 2018

Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics.