Unified framework for path-planning and task-planning for autonomous robots

Muñoz, Pablo; Barrero, David F.

doi:10.1016/j.robot.2016.04.010

Cited by 40 publications

(17 citation statements)

References 17 publications

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“…However, since a number of uncertainties may occur along the trajectory in practical applications, the real-time trajectory modification module is added to the system such that the formation is able to deal with emergency situations such as a suddenly emerged obstacle. A good example of integrating path planning capability with the task-planning requirement can be found at Munoz et al 38 , where a unified framework has been proposed for exploration missions. Also, in Mahmoudzadeh et al 39 , a novel combinatorial conflict-free task assignment and path planning strategy has been proposed for largescale underwater missions and based upon such a strategy, Zhu et al 40 incorporated a biologically inspired neural network (BINN) into the task-allocation algorithm to address the dynamics constraints of the vehicles when generating the path.…”

Section: System Architecture Of Multi-vehicle Formationmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

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Section: System Architecture Of Multi-vehicle Formationmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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“…Critical path and resource levelling methods for task scheduling have been integrated by Hasgül et al (2009) and a three-level architecture has been applied on a mobile robot platform for planning and execution under uncertainty (Hanheide et al 2017). Hybrid approaches for task and path planning for a mobile robot team have been also proposed using heuristics (Michalos et al 2016;Muñoz, R-Moreno, and Barrero 2016;Bidot et al 2017). However, the existing approaches are focusing on scheduling low-level mobile robot actions and need expansion to address the needs of a material supply system at the factory level.…”

Section: Task Planning and Scheduling For Materials Supply Operationsmentioning

confidence: 99%

Scheduling of smart intra – factory material supply operations using mobile robots

Kousi

Koukas

Michalos

et al. 2018

International Journal of Production Research

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This paper presents the design and prototype implementation of a service-based control system responsible for the material supply operations planning and coordination in assembly lines. The material supply processes are carried out by autonomous mobile units that are responsible for the transportation of the consumables from the warehouse to the production stations. The plan generation based on time and inventory level driven criteria is automatically carried out by a web-based software that can also distribute the derived plan to the autonomous mobile units. The proposed system was implemented on a case inspired by an actual production line from the automotive assembly sector. Discrete event simulation has been employed for the investigated production system, to derive the specifications for the mobile units (e.g. number of boxes that can carry simultaneously) that may serve the system efficiently. The results indicate that the proposed architecture integrated with the discussed mobile assistant units may provide high quality solutions with respect to the end user criteria.

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“…Patle et al [39] propose an approach based on matrix-binary codes with a genetic algorithm to implement CPP including manipulator control and theoretical ideas to solve the CPP problem. Munoz et al have proposed a unified framework for path and task planning for autonomous robots [40].…”

Section: Related Researchmentioning

confidence: 99%

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Hai

Moore

2018

Machines

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Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.

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Unified framework for path-planning and task-planning for autonomous robots

Cited by 40 publications

References 17 publications

A survey of formation control and motion planning of multiple unmanned vehicles

A survey of formation control and motion planning of multiple unmanned vehicles

Scheduling of smart intra – factory material supply operations using mobile robots

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

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