scholarly journals Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

2021 ◽  
Vol 10 (3) ◽  
pp. 1-25
Author(s):  
Lawrence H. Kim ◽  
Sean Follmer
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Optimal Solution ◽  
Robot Motion ◽  
Vision Science ◽  
Swarm Robots ◽  
Different Types ◽  
Behavior Based ◽  
Swarm Robot

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

2019 ◽  
Vol 31 (4) ◽  
pp. 520-525 ◽  
Author(s):  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Reinforcement Learning ◽  
Collective Behavior ◽  
Design Methodology ◽  
Learning Performance ◽  
Centralized Control ◽  
Robot System ◽  
Swarm Robots ◽  
Robot Systems ◽  
Swarm Robot ◽  
Typical Design

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

scholarly journals Behavior-Based Formation Control of Swarm Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Dongdong Xu ◽  
Xingnan Zhang ◽  
Zhangqing Zhu ◽  
Chunlin Chen ◽  
Pei Yang
Keyword(s):  
Formation Control ◽  
Large Scale ◽  
Research Field ◽  
Initial Formation ◽  
Swarm Robots ◽  
Robot Systems ◽  
Potential Applications ◽  
Behavior Based ◽  
Swarm Robot ◽  
Searching Method

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.

Space robot motion path planning based on fuzzy control algorithm

2021 ◽  
pp. 1-8
Author(s):  
Baoyu Shi ◽  
Hongtao Wu
Keyword(s):  
Path Planning ◽  
Fuzzy Control ◽  
Obstacle Avoidance ◽  
Control Algorithm ◽  
Space Robot ◽  
Robot Motion ◽  
Robot Path Planning ◽  
Behavior Based ◽  
Fuzzy Control Algorithm ◽  
Robot Path

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

scholarly journals Linear programming measures for solving the problem of a linear division of convex multiplayers.

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Sarmad H. Ali ◽  
Osamah A. Ali ◽  
Samir C. Ajmi
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Optimal Solution ◽  
Optimal Method ◽  
Linear Separation ◽  
Different Types ◽  
Simplex Methods

In this research, we are trying to solve Simplex methods which are used for successively improving solution and finding the optimal solution, by using different types of methods Linear, the concept of linear separation is widely used in the study of machine learning, through this study we will find the optimal method to solve by comparing the time consumed by both Quadric and Fisher methods.

scholarly journals Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 354
Author(s):  
Ahmed T. Sadiq Al-Obaidi ◽  
Hasanen S. Abdullah ◽  
Zied O. Ahmed
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Organizational Skills ◽  
Suricata Suricatta ◽  
Kalahari Desert ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

scholarly journals Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5164
Author(s):  
Changsun Shin ◽  
Meonghun Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Network Performance ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Link Quality ◽  
Wireless Sensor ◽  
Control Message

The swarm intelligence (SI)-based bio-inspired algorithm demonstrates features of heterogeneous individual agents, such as stability, scalability, and adaptability, in distributed and autonomous environments. The said algorithm will be applied to the communication network environment to overcome the limitations of wireless sensor networks (WSNs). Herein, the swarm-intelligence-centric routing algorithm (SICROA) is presented for use in WSNs that aim to leverage the advantages of the ant colony optimization (ACO) algorithm. The proposed routing protocol addresses the problems of the ad hoc on-demand distance vector (AODV) and improves routing performance via collision avoidance, link-quality prediction, and maintenance methods. The proposed method was found to improve network performance by replacing the periodic “Hello” message with an interrupt that facilitates the prediction and detection of link disconnections. Consequently, the overall network performance can be further improved by prescribing appropriate procedures for processing each control message. Therefore, it is inferred that the proposed SI-based approach provides an optimal solution to problems encountered in a complex environment, while operating in a distributed manner and adhering to simple rules of behavior.

scholarly journals Determining the vehicles routes by considering its different types and capacity (heterogeneous) to minimize the total distribution cost

2018 ◽  
Vol 154 ◽  
pp. 01069
Author(s):  
Nurhidayat ◽  
Annie Purwani
Keyword(s):  
Optimal Solution ◽  
Fixed Cost ◽  
Routing Problem ◽  
Total Cost ◽  
Vehicle Type ◽  
Different Types ◽  
Insertion Algorithm ◽  
Calculation Results ◽  
Distribution Cost ◽  
Alternative Solutions

Packaged sugar is one of the products in sugar cane manufacture, PT Madubaru Yogyakarta. Currently, the company deals with a high distribution cost because there is no plan to determine the vehicle route, vehicle type, and capacity for distributing the product. In this research, the optimum route of the distributing vehicles is developed. The company has three different types and capacity of vehicles: L300, HD, and PS. A problem in determining the distribution route here is called as Vehicle Routing Problem (VRP). The basic form of classic VRP says that all vehicles owned by a company have the same capacity (homogenous), meanwhile not all companies have the vehicles with same capacity. The heterogeneous variant is used to minimize the fixed cost of vehicles and distribution variant cost using Sequential Insertion Algorithm. This research has three purposes; are minimizing the number of vehicles used (NV), Total time of Completion tour (TCT) and Distribution Total Cost (TCD). The results based on a test calculation of a shipment date (August 19, 2016) of the company show three alternative solutions to distribute the packaged sugar to 12 consumers. The third alternative solution is the optimal solution and chosen as the decision result of the packaged sugar shipment. Based on the calculation results, it is needed 2 vehicles type HD with capacity 7,000 kg and type PS with capacity 3,500 kg, with total time of completion tour (TCT) is 828.49 minutes or 13.81 hours, and distribution total cost is IDR. 959,011.

scholarly journals Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 980
Author(s):  
Gustavo Meirelles ◽  
Bruno Brentan ◽  
Joaquín Izquierdo ◽  
Edevar Luvizotto
Keyword(s):  
Systems Engineering ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Sensitivity Analyses ◽  
Grand Tour ◽  
Agent Based ◽  
Local Optimal Solution ◽  
Classical Optimization

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.

scholarly journals Population Diversity Maintenance In Brain Storm Optimization Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 83-97 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin ◽  
Qingyu Zhang ◽  
Ruibin Bai
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Search Results ◽  
Brain Storm Optimization ◽  
Convergence And Divergence ◽  
Definition Of ◽  
The Brain

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

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