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

Moon, Jiyoun; Lee, Beom-Hee

doi:10.3390/app9183789

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…Basing on the knowledge base, the intelligent task understanding module and the scene and situation understanding module will cooperate to generate the planning problem and constraints for the vehicle planning system. The scene and situation understanding module is mainly based scene graph generation technique with natural language-based semantic description [51]. We mainly focus on the design of the intelligent task understanding module and the nested vehicle routing planning module.…”

Section: Formulation and Solution Methodology A Framework Overviewmentioning

confidence: 99%

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

et al. 2020

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Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this paper, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers' distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

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Section: Formulation and Solution Methodology A Framework Overviewmentioning

confidence: 99%

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

et al. 2020

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“…Other examples of custom implementations derived from PDDL are based on action graphs (Harman and Simoens 2020). Gragera et al ( 2019) proposed a PDDL-based planner for social robotics; Moon and Lee (2019) investigated coordination of unnamed ground and aerial vehicles; Muñoz et al (2019) implemented an integrated framework for robotic manipulator, surveillance and rover exploration; Ma et al (2016) focused on mobile robots in the ROS framework; SHOP Nau et al (1999) was proposed for a state-of-the-art implemen-tation of Hierarchical Task Network (HTN) (Erol 1996), a popular and efficient formalism for task planning with sub-goals.…”

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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“…J. Moon and B. Lee present a cooperation framework between the UAV and unmanned ground vehicle (UGV) that connects deep learning techniques and a symbolic planner for heterogeneous robots by using the planning domain definition language (PDDL) planning with natural language-based scene understanding method [20]. W. Yue, X. Guan, and L. Wang present the UAV cooperative search mission for multi-dynamic targets in sea areas using a reinforcement learning (RL) algorithm [21].…”

Section: Advanced Uav Technologiesmentioning

confidence: 99%

Special Issue on Unmanned Aerial Vehicles (UAVs)

Jung

2020

Applied Sciences

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This editorial paper was a special issue of Applied Sciences belonging to the section of mechanical engineering in MDPI journal and summarized the collected manuscripts regarding the unmanned aerial vehicles (UAVs) related technologies, including communication, control, collision avoidance, modeling, path planning, human-machine interface (HMI), artificial intelligence (AI), etc. Chronologically, this special issue was started to be coordinated at the end of Oct 2018, prepared for a month and opened to collect manuscripts from the middle of Nov 2018 until the end of Dec 2019. During almost a year, 26 papers were published online out of 50 submitted papers which results in 52% acceptance rate.

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

Cited by 6 publications

References 32 publications

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

Logic programming for deliberative robotic task planning

Special Issue on Unmanned Aerial Vehicles (UAVs)

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