With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

An integrated biologically inspired self-organizing map (SOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in 3-D underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the SOM neural networks. The task assignment and path planning aim to arrange a team of AUVs to visit all appointed target locations, while assuring obstacle avoidance without speed jump. The SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then, in order to avoid obstacles and speed jump for each AUV that visits the corresponding target location, the BINN is utilized to update weights of the winner of SOM, and achieve AUVs path planning and effective navigation. The effectiveness of the proposed hybrid model is validated by simulation studies.

Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path Planning of an AUV System

https://pure.rug.nl/ws/files/56896934/2018_Information_Sciences_Task_Assignment.pdf

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

Intending to mimic the operating mechanism of biological neural systems, a self organizing map-based approach to task assignment of a swarm of robots in 3-D dynamic environments is proposed in this paper. This approach integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of a swarm of robots in 3-D environments under uncertain situations, such as when some robots are presented in or broken down or when more than one robot is needed for some special task locations. A Bezier path optimizing algorithm and a parameter adjusting algorithm are integrated in this paper. It is capable of reducing the complexity of the robot navigation control and limiting the number of convergence iterations. The simulation results with different environments demonstrate the effectiveness of the proposed approach.

A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments

The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently a new hunting algorithm based on a bio-inspired neural network has bee...

https://journals.sagepub.com/doi/pdf/10.5772/61555

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

A good task assignment is an important guarantee to achieve great combat effectiveness. This paper investigates the task assignment problem, where the value of the targets is time changing in the battlefield, and presents a solution approach that is a combination of two algorithms: the multidestination route planning algorithm based on dynamic programming and the multisubgroup ant colony algorithm (MSACO). The two algorithms coordinately solve the task assignment problem. The route planning algorithm can obtain available routes between any two targets and provide reasonable routing information for MSACO. Then, the ant colony algorithm is applied to solve the task assignment problem. To solve the task assignment problem in the battlefield environment, several key technologies are introduced to improve the traditional ant colony algorithm, which include the subgroup selection strategy, the dynamic candidate aggregate policy, the state transferring policy, and the information-element updating mechanism. Simulation results show that the proposed approach can produce a reasonable and available plan for all the test cases in short computational time.

Liu Zhong

Papers

A Task Assignment Algorithm for Multiple Aerial Vehicles to Attack Targets With Dynamic Values