GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

Miloradović, Branko; Çürüklü, Baran; Ekström, Mikael; Papadopoulos, Alessandro Vittorio

doi:10.1109/tcyb.2021.3070913

Cited by 20 publications

(10 citation statements)

References 37 publications

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“…Unfortunately, the MILP formulation is an NP-hard problem ( Miloradović et al, 2020 ; Miloradović et al, 2021 ), and computing a solution can be time-consuming. Compared to MILP solvers, PDDL based planners tend to be more efficient for RTSG models with more constraints but less efficient for models with fewer constraints ( Lager et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Task Roadmaps: Speeding up Task Replanning

et al. 2022

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Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot’s operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a long-sighted complement to path replanning, which is mostly concerned with avoiding unexpected obstacles that can lead to potentially unsafe situations. This paper focuses on task replanning as a way to dynamically adjust the robot behaviour to the continuously evolving environment in which it is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the concept of Task roadmaps as a method to replan tasks by leveraging an offline generated search space. A graph-based model of the robot application is converted to a task scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning Domain Definition Language (PDDL). The B&B approach is proposed to compute the task roadmap, which is then reused to replan for unforeseeable events. The optimality and efficiency of this replanning approach are demonstrated in a simulation-based experiment with a mobile manipulator in a kitting application. In this study, the proposed B&B Task Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based planner.

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Section: Introductionmentioning

confidence: 99%

Task Roadmaps: Speeding up Task Replanning

et al. 2022

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“…Both problems have been widely studied in the literature over years [4], [5] and they can be mainly grouped into two classes, depending on whether the task allocation problem is addressed in a centralized or decentralized fashion. Centralized solutions, in the form of constrained optimization problems, allow to retrieve the best schedule for each robot [6], [7]. However, these solutions suffer from the high computational burden required to solve the optimization problem.…”

Section: A Related Workmentioning

confidence: 99%

Mission Planning and Execution in Heterogeneous Teams of Aerial Robots supporting Power Line Inspection Operations

Calvo

Silano

Capitán

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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A software architecture aimed at coordinating a team of heterogeneous aerial vehicles for inspection and maintenance operations in high-voltage power line scenarios is presented in this paper. A hierarchical approach deals with high-level tasks by planning and executing complex missions requiring vehicles to support human operators. A resource-constrained problem allows distributing tasks among the team taking into account vehicles' capabilities and battery constraints. Besides, Behavior Trees (BTs) are in charge of mission execution, triggering replanning operations in case of unforeseen events, such as vehicle faults or communication drop-outs. The feasibility and validity of the approach are showcased through realistic simulations achieved in Gazebo.

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“…According to different constraints, the traditional task allocation problem can be subdivided into the vehicle routing problem (VRP) [14][15][16] , the multi-travel salesman problem (MTSP) [17][18][19] , and the multi-processor resource allocation problem (MPRA) 20,21 . As the number of UAV swarms increases on a large scale and the amount of computation increases, swarm intelligent optimization algorithms, such as particle swarm optimization algorithm (PSO) [22][23][24] , genetic algorithm (GA) [25][26][27][28] , grey wolf algorithm (GWO) [29][30][31] , ant colony optimization (ACO) [32][33][34] etc., are being used to increase the optimization speed, which mitigates the disadvantage of excessive computation caused by the increase in swarms. For the task allocation problem of a UAV swarm under an intentional attack, however, the classic task allocation optimization method struggles to account for the influence of the dynamic change of the optimization index before and after the attack.…”

Section: Introductionmentioning

confidence: 99%

A Resilient Task Allocation Method for UAV Swarm Based on PSO-ILP Bilevel Nested Optimization

Zeng¹,

Ren²,

Sun³

et al. 2023

Preprint

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Traditional task allocation methods are threatened by the complexity and adversarial nature of modern battlefields. This work focuses on the modeling, optimization, and simulation verification of UAV swarm multi-domain fighting under the constraint of task resilience in order to address the issues created by various ways of deliberate enemy attack. Initially, a novel idea of equivalent load is proposed, considering it as the fundamental unit of reconnaissance, assault, communication, and other activities, in order to construct the capability load matrix of our single UAV and the needed load matrix of attacking each fighting unit in each battle region. Then, by integrating the strike probability and task completion degree, the task resilience capability index was developed, which improved the current UAV swarm task resilience measurement process. Due to the difficulty of traditional task allocation optimization methods in dealing with dynamic changes of optimization indexes before and after attacks, a resilience compensation load relaxation variable was added to the traditional Integral Linear Programming (ILP) problem description model of a UAV swarm. On the basis of a bilevel nested structure, a task allocation optimization method is created. Before an assault, the lower layer's ILP optimizer uses the swarm load cost as the target. The uppermost layer is comprised of Particle Swarm Optimization (PSO), which targets the comprehensive indices of UAV swarm load cost and task resilience after attack. It effectively resolves the multi-objective optimization problem of UAV swarms taking task difficulty into account. Ultimately, the test scenarios of three conflict domains, five basic battle units, and five load kinds were constructed, and the Ranchester battle model was used to simulate and validate the rationale and efficacy of the bilevel nested optimization method based on PSO-ILP.

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GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

Cited by 20 publications

References 37 publications

Task Roadmaps: Speeding up Task Replanning

Task Roadmaps: Speeding up Task Replanning

Mission Planning and Execution in Heterogeneous Teams of Aerial Robots supporting Power Line Inspection Operations

A Resilient Task Allocation Method for UAV Swarm Based on PSO-ILP Bilevel Nested Optimization

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