A Global/Local Path Planner for Multi-Robot Systems with Uncertain Robot Localization

Almeida, João Paulo Lima Silva de; Nakashima, Renan Taizo; Neves-Jr, Flávio; Arruda, Lúcia Valéria Ramos de

doi:10.1007/s10846-020-01196-y

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…The literature features various bio-inspired algorithms, among which genetic algorithms (GAs) stand out [53][54][55], as do Particle Swarm Optimization (PSO) [57], Ant Colony Optimization (ACO) [59], Bacterial Foraging Optimization (BFO) [56], Ant Lion Optimization (ALO) [58], and the grasshopper algorithm [60].…”

Section: Bio-inspired Algorithmsmentioning

confidence: 99%

“…They use a population of individuals representing potential solutions, and apply selection, crossover, and mutation operators to evolve and improve the solutions over generations. In [55], a global path planning strategy for multi-robot systems with differential drive and Bluetooth communication solved by a genetic algorithm was developed. The efficiency of the proposed technique was validated through experimental study implemented in a semi-unknown environment with static and dynamic obstacles.…”

Section: Bio-inspired Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

AbuJabal,

Rabie,

Baziyad

et al. 2024

Electronics

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A vast amount of research has been conducted on path planning over recent decades, driven by the complexity of achieving optimal solutions. This paper reviews multi-robot path planning approaches and presents the path planning algorithms for various types of robots. Multi-robot path planning approaches have been classified as deterministic approaches, artificial intelligence (AI)-based approaches, and hybrid approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. However, multi-robot systems suffer from well-known problems such as the number of robots in the system, energy efficiency, fault tolerance and robustness, and dynamic targets. Deploying systems with multiple interacting robots offers numerous advantages. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot systems.

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Section: Bio-inspired Algorithmsmentioning

confidence: 99%

Section: Bio-inspired Algorithmsmentioning

confidence: 99%

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

AbuJabal,

Rabie,

Baziyad

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…In the above formulation, constraints (1) ensure that cardinality of in-degree and out-degree matches for each UV in a refueling station. Constraints (2) ensure that all the UVs are used for the mission, i.e, each UV leave and return to the base station. Constraints (3) help a feasible solution to stay connected, and constraints (4) are indicator type where it forces z m r to take a value of 1 if an m th UV uses the refueling station r ∈ R \ {r 0 }.…”

Section: Risk-neutral Two-stage Stochastic Programming Recourse Modelmentioning

confidence: 99%

“…Since they are hard to solve, and NP-hard in general, mission planning problems are solved offline before the start of a mission. Using the high-level routing plan for each UV, the low-level 'path planning algorithms' solve the challenge of finding an optimal trajectory between a pair of source and destination while considering obstacles, and provide closed-loop control signals to each UV so that they can follow the trajectory with minimum deviations [2,7,40]. Due to trackability reasons, the complexities in low-level planning are not considered in the high-level planning.…”

mentioning

confidence: 99%

UV mission planning under uncertainty in vehicles' availability

Venkatachalam¹,

Smereka²

2020

Preprint

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Heterogeneous unmanned vehicles (UVs) are used in various defense and civil applications. Some of the civil applications of UVs for gathering data and monitoring include civil infrastructure management, agriculture, public safety, law enforcement, disaster relief, and transportation. This paper presents a twostage stochastic model for a fuel-constrained UV mission planning problem with multiple refueling stations under uncertainty in availability of UVs. Given a set of points of interests (POI), a set of refueling stations for UVs, and a base station where the UVs are stationed and their availability is random, the objective is to determine route for each UV starting and terminating at the base station such that overall incentives collected by visiting POIs is maximized. We present an outer approximation based decomposition algorithm to solve large instances, and perform extensive computational experiments using random instances. Additionally, a data driven simulation study is performed using robot operating system (ROS) framework to corroborate the use of the stochastic programming approach.Advances in wireless networks, sensing, and robotics have led to various applications for Unmanned Vehicles (UVs). Crop monitoring [32,37,39,38], forest fire monitoring [6], ecosystem management [8,42], ocean bathymetry [14] are some of the environmental sensing applications. Similarly, disaster management [23] and border surveillance [24,21] are some of the civil security applications. UVs are frequently used by

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“…The use of small differential robots for navigation in unknown environments has been an area of interest for researchers for many years [8], [9]. The ability of these robots to navigate in tight spaces and uneven terrains makes them suitable for exploring unknown environments [10].…”

Section: Introductionmentioning

confidence: 99%

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

Sarmiento¹,

Jacinto²,

Ariza³

2023

IJACSA

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This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcontroller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and costeffective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot's motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

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A Global/Local Path Planner for Multi-Robot Systems with Uncertain Robot Localization

Cited by 13 publications

References 22 publications

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

UV mission planning under uncertainty in vehicles' availability

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

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