A ROSPlan-Based Multi-Robot Navigation System

Miranda, Dannilo Samuel Silva; Souza, Luiz Edival de; Bastos, Guilherme Sousa

doi:10.1109/lars/sbr/wre.2018.00053

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Basing on ROS infrastructure offers a considerable implementative advantage for application with most robotic systems. ROSPlan has been applied in several scenarios, e.g., by Miranda et al (2018) for multi-robot navigation; Sanelli et al (2017), integrated with Petri nets for human-robot interaction; Escudero-Rodrigo and Alquezar (2016), tackling the anchoring problem, that is matching abstract symbols from high-level knowledge to perceptions and executed actions, in order to automatically generate code for ROS nodes.…”

Section: Other Plannersmentioning

confidence: 99%

Logic programming for deliberative robotic task planning

2023

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Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.

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Section: Other Plannersmentioning

confidence: 99%

Logic programming for deliberative robotic task planning

2023

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“…It has had 28 contributors and more than a thousand commits throughout the last six years of development. It has fueled dozens of projects in as many robots, both land [11] [12], air [13], and marine [14]. Much of the success of ROSPlan is because it is the reference package for Planning in ROS, which is the de facto standard in robot programming.…”

Section: Introductionmentioning

confidence: 99%

PlanSys2: A Planning System Framework for ROS2

Martı́n¹,

Ginés²,

Olivera³

et al. 2021

Preprint

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Autonomous robots need to plan the tasks they carry out to fulfill their missions. The missions' increasing complexity does not let human designers anticipate all the possible situations, so traditional control systems based on state machines are not enough. This paper contains a description of the ROS2 Planning System (PlanSys2 in short), a framework for symbolic planning that incorporates novel approaches for execution on robots working in demanding environments. PlanSys2 aims to be the reference task planning framework in ROS2, the latest version of the de facto standard in robotics software development. Among its main features, it can be highlighted the optimized execution, based on Behavior Trees, of plans through a new actions auction protocol and its multi-robot planning capabilities. It already has a small but growing community of users and developers, and this document is a summary of the design and capabilities of this project.

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“…In particular, planning domain definition language (PDDL) is used as a standardized artificial intelligence planning language [2] and provides flexibility when planning actions for robots to achieve mission goals [3]. Miranda et al [4] embedded a symbolic task planner using PDDL in the robot operating system (ROS) for multi-robot navigation. Zhang et al [5] presented a multi-robot symbolic planning system with an iterative interdependent algorithm to find the optimal plans that minimize overall cost.…”

Section: Introductionmentioning

confidence: 99%

PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

Moon

Lee

2019

Applied Sciences

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Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.

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A ROSPlan-Based Multi-Robot Navigation System

Cited by 5 publications

References 11 publications

Logic programming for deliberative robotic task planning

Logic programming for deliberative robotic task planning

PlanSys2: A Planning System Framework for ROS2

PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

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