A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

Godio, Simone; Primatesta, Stefano; Guglieri, Giorgio; Dovis, Fabio

doi:10.3390/info12020051

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…Both models could achieve obstacle avoidance in real-time and the complexity is squarely proportional to the degree of discretization, O(n 2 ), where n is the number of neurons in the system. The BINN has also been utilized to deal with the problem of multi-robot formation control in coverage planning tasks [325]. Still, somehow the optimal path is planned close enough to the obstacles or multirobot near-collision situation, leading to difficulty avoiding the fast-moving obstacles.…”

Section: Discussion and Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: Discussion and Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…In addition to the above literature review, generalized methodologies exist that propose novel approaches that would improve facets of the SAR process. These include using bio-inspired algorithms for area coverage [42] formation tracking [43] and environment exploration [44], updated and merged observation maps or information exchange pathways [45], and efficient task planning [46,47]. Frameworks such as [41] that propose automatic replacement of lost UAV agents are scarce.…”

Section: Summary Of Recent Literature On Multi-robot Sarmentioning

confidence: 99%

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Phadke,

Medrano

2024

Aerosp. Res. Commun.

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Resilient UAV (Unmanned Aerial Vehicle) swarm operations are a complex research topic where the dynamic environments in which they work significantly increase the chance of systemic failure due to disruptions. Most existing SAR (Search and Rescue) frameworks for UAV swarms are application-specific, focusing on rescuing external non-swarm agents, but if an agent in the swarm is lost, there is inadequate research to account for the resiliency of the UAV swarm itself. This study describes the design and deployment of a Swarm Specific SAR (SS-SAR) framework focused on UAV swarm agents. This framework functions as a resilient mechanism by locating and attempting to reconnect communications with lost UAV swarm agents. The developed framework was assessed over a series of performance tests and environments, both real-world hardware and simulation experiments. Experimental results showed successful recovery rates in the range of 40%–60% of all total flights conducted, indicating that UAV swarms can be made more resilient by including methods to recover distressed agents. Decision-based modular frameworks such as the one proposed here lay the groundwork for future development in attempts to consider the swarm agents in the search and rescue process.

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“…Specifically, the model proposes a stable method that converges to a goal. The model is composed of various algorithms that utilize evolutionary techniques and neural networks to describe the path planning techniques used by UAVs [ 52 ].…”

Section: Bio-inspired Algorithms For Uav Path Planningmentioning

confidence: 99%

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Poudel

Arafat

Moh

2023

Sensors

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Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

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A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

Cited by 15 publications

References 29 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

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