Unknown environment exploration of multi-robot system with the FORDPSO

Wang, Dongshu; Wang, Haitao; Liu, Lei

doi:10.1016/j.swevo.2015.09.004

Cited by 45 publications

(17 citation statements)

References 31 publications

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“…Recently, the metaheuristic field found its application in robotics [15]. It made a significant impact on the methodology and application areas for mobile and multi-robot systems.…”

Section: Related Workmentioning

confidence: 99%

“…In (14), the vector ⃗ ⃗⃗ computes the distance of the current whale ⃗ to the random whale position. Here, the stochastic parameter ⃗ gives some additional deviations for ⃗ ⃗⃗ that allows finding the ⃗ ( + 1) position around the ⃗ solution (15).…”

Section: ) Mathematical Modelmentioning

confidence: 99%

“…if |A| < 1 12: When the best and random whale solutions are found, the next ⃗( + 1) can be computed using (13), (15), or (17) in lines 13, 16, and 20, respectively. The decision on what method is applied in the bubble-net hunting and which phase is the search (exploration or exploitation) depends on the and ⃗ parameters.…”

Section: ) Proposed Exploration Algorithm Using Whale Optimization Amentioning

confidence: 99%

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Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

2020

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This paper proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.

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“…Recently, the metaheuristic field found its application in robotics [15]. It made a significant impact on the methodology and application areas for mobile and multi-robot systems.…”

Section: Related Workmentioning

confidence: 99%

Section: ) Mathematical Modelmentioning

confidence: 99%

Section: ) Proposed Exploration Algorithm Using Whale Optimization Amentioning

confidence: 99%

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Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

2020

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“…The clustering provides a direction of robot motion, while the nature-inspired approaches involve exploring the unknown area. The study of [36] applied a combination of PSO, fractional calculus, and fuzzy inference system. They compared their results with other six other PSO variations that showed effective multi-robot exploration.…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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“…On the other hand, multi-agent systems are very popular for their robustness and self-adaptation capacity [2]. They are getting more and more emphasis and research focus [3,4]. Productivity enhancing and vehicle safety are the two main factors that should be satisfied in all stages of multi-agent planning [5].…”

Section: Introductionmentioning

confidence: 99%

A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems

2019

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Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environments. In both approaches, the Particle Swarm Optimization-based method is applied for path planning. The path planning approach combined with the obstacle avoidance strategy is integrated with the task assignment problem. In one framework, the Compromise View model is used for completing the tasks and a combination of clustering method with the Nearest-Neighbour Search model is used to assign tasks to the other framework. The frameworks are compared in terms of computational time and the resulting path length. Results indicate that the Nearest-Neighbour Search model is much faster than the Compromise View model. However, the Nearest-Neighbour Search model generates longer paths to accomplish the mission. By following the Nearest-Neighbour Search approach, agents can successfully accomplish their mission, even under uncertainties such as malfunction of individual agents. The Nearest-Neighbour Search framework is highly effective due to its reactive structure. As per requirements, to save time, after completing its own tasks, one agent can complete the remaining tasks of other agents. The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems.

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Unknown environment exploration of multi-robot system with the FORDPSO

Cited by 45 publications

References 31 publications

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems

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