PlanSys2: A Planning System Framework for ROS2

Plan-Based Control of Robotic Agents, 2002

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.

Automated planners have been employed in a variety of robotics applications. However, there still remains a distinct divide between task planning, or high-level planning, and its counterparts in robotics. In particular, navigation and dialogue planning have emerged as important concerns in the quest to make realistic end-to-end robotic systems a reality. However, in the absence of a unifying problem to solve, collaborations between these three fields have been sparse and mostly narrow and project-driven. In this paper, we discuss Human-Robot Teaming as that unifying problem, and outline via a simple example the various sub-fields of the different kinds of planning that interact naturally to produce a solution to the overall problem. Our hope is to spur the various, fragmented planning communities into further collaboration by highlighting the rich potential of these interactions.

Lecture Notes in Computer Science, 2004

We present here an overview of several planning techniques in robotics. We will not be concerned with the synthesis of abstract mission and task plans, using well known classical and other domain-independent planning techniques. We will mainly focus on to how refine such abstract plans into robust sensory-motor actions and on some planning techniques that can be useful for that. The paper introduces the important and mature area of path and motion planning. It illustrates the usefulness of HTN and MDP planning techniques for the design of a high level controller for a mobile robot. 1

Journal of King Saud University - Computer and Information Sciences, 2021

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2008

The aim of this paper is to describe a novel representation framework for high level robot and multi-robot programming, called Petri Net Plans (PNP), that allows for representing all the action features that are needed for describing complex plans in dynamic environments. We provide a sound and complete execution algorithm for PNPs based on the semantics of Petri nets. Moreover, we show that multi-robot PNPs allow for a sound and complete distributed execution algorithm, given that a reliable communication channel is provided. PNPs have been used for describing effective plans for actual robotic agents which inhabit dynamic, partially observable and unpredictable environments, and experimented in different application scenarios.

Artificial Intelligence and Soft …, 2008

Abstract. A multiagent system to support a mobile robot motion plan-ning has been presented. Baptized MARCoPlan (MutiAgent Remote Control motion Planning), this system deals with optimizing robot path. Considered as an agent, the robot has to optimize its motion ...

2013

Planning for autonomous robots differs from classical planning, because actions are not behaving deterministically. In a real world environment there are too many parameters and too many cases to handle before the plan execution. Nevertheless planning is needed to give robots the flexibility to work in unknown or not completely known environments. Plan execution can be seen as a flexible way of handling unknown and/or changing environments. Here, during runtime, sensors and reasoners can be asked to give necessary information. To cover the uncertainty in the real world, there is need for more than pure logical reasoning . In order to close this gap, we want to extend the CRAM Plan Language (CPL) so that it can ask probabilistic world models which is the best way to continue under the current conditions and beliefs. These world models should be ultimately learned and trained autonomously by the robot. For this the robot needs to log data in an appropriate way as well as use this data...

2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

In this study, we propose task planning framework for multiple robots that builds on a behavior tree (BT). BTs communicate with a data distribution service (DDS) to send and receive data. Since the standard BT derived from one root node with a single tick is unsuitable for multiple robots, a novel type of BT action and improved nodes are proposed to control multiple robots through a DDS asynchronously. To plan tasks for robots efficiently, a single task planning unit is implemented with the proposed task types. The task planning unit assigns tasks to each robot simultaneously through a single coalesced BT. If any robot falls into a fault while performing its assigned task, another BT embedded in the robot is executed; the robot enters the recovery mode in order to overcome the fault. To perform this function, the action in the BT corresponding to the task is defined as a variable, which is shared with the DDS so that any action can be exchanged between the task planning unit and robots. To show the feasibility of our framework in a real-world application, three mobile robots were experimentally coordinated for them to travel alternately to four goal positions by the proposed single task planning unit via a DDS. Index Terms-behavior tree, supervisory control, data distribution service This study was supported by Hyundai Robotics. All robots for testing, demonstration, and environmental settings were operated in the Hyundai Robotics Engineering laboratory. Seungwoo Jeong, Taekwon Ga, and Jongeun Choi are with the

National Conference on Artificial Intelligence, 2018

In this paper, we are concerned with making the execution of abstract action plans for robotic agents more robust. To this end, we propose to model the internals of a robot system and its ties to the actions that the robot can perform. Based on these models, we propose an online transformation of an abstract plan into executable actions conforming with system specifics. With our framework, we aim to achieve two goals. First, modeling the system internals is beneficial in its own right in order to achieve long term autonomy, system transparency, and comprehensibility. Second, separating the system details from determining the course of action on an abstract level leverages the use of planning for actual robotic systems.

International Conference on Automated Planning and Scheduling (ICAPS), Workshop on Planning and Robotics (PlanRob), 2013

We present an application of Hierarchical Task Network (HTN) planning to create robot execution plans, that are adapted to the environment and the robot hardware from abstract task descriptions. Our main intention is to show that different robotic platforms can make use of the same high level symbolic task description. As an off-the-shelf planning component, the SHOP2 HTN planner is adopted. All the domain knowledge is encoded in the Web Ontology Language (OWL) and stored in a world wide accessible database, which allows multiple systems to reuse and improve upon this knowledge. For task execution, the execution plan is generated using the CRAM plan language (CPL). We demonstrate the functionality of the system in executing a pick-and-place task in a simulated environment with two different service robots, the TU/e Amigo robot prototype and the Fraunhofer IPA Care-O-Bot 3. The experiment shows that although the robots differ in hardware capabilities, the use of HTN planning adds information that is crucial for successful task execution and enables both systems to successfully execute the instructed task.