Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs

https://mediatum.ub.tum.de/doc/1429191/file.pdf

Control Sharing in Human-Robot Team Interaction

In this paper, we study cohesive flocking and formation flocking of multiple robotic fish swimming in the water surface under the guidance of only one leader with zero-value external input. Combining consensus algorithms and attraction/repulsion functions, a distributed flocking algorithm is proposed for the robotic fish system to execute the cohesive flocking task. According to the LaSalle–Krasovskii invariance principle, the proposed algorithm enables followers to asymptotically track the leader's velocity and approach the equilibrium distances with their neighbors, provided that the initial interaction network of the system is a leader–follower connected graph. Furthermore, by adding the information of a desired formation topology to the potential function term, the proposed algorithm can be extended to solve arbitrarily shaped formation flocking problem of multiple robotic fish. The experimental results demonstrate that the proposed approaches are effective for three robotic fish. Finally, several simulation examples are given to verify the functionality of the proposed approaches for a larger system with ten agents.

Leader–Follower Flocking of Multiple Robotic Fish

Motion Control and Motion Coordination of Bionic Robotic Fish: A Review

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

The presence of unknown physical interaction between the patients’ body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach’s effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

Fuzzy Approximation-Based Task-Space Control of Robot Manipulators With Remote Center of Motion Constraint

This paper presents a coordination method for multiple biomimetic robotic fish in underwater transport task. Based on our robotic fish prototype, we further investigate the coordination problems of multiple robotic fish. Considering the kinematic constraints of the robotic fish, and the unstructured and dynamic characteristics of the water environment, we utilize the limit cycle approach to control the posture of the fish and realize collision avoidance. To control the transport orientation in the particular underwater environment, the fuzzy logic method is adopted. Experimental results of transportation performed by three robotic fish validate the effectiveness of the proposed method.

Coordinated Transport by Multiple Biomimetic Robotic Fish in Underwater Environment

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown environments. The method is applicable for mediate- to large-scaled robot groups and tasks. A hierarchical architecture for task assignment is established for each individual robot. In the higher hierarchy, the self-reinforcement learning model inspired by the behaviors of social insects is employed to differentiate the initially identical robots into different kinds of high-level task "specialists"; while in the lower hierarchy, ant system algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, "local blackboard" communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environment perturbations and robustly to the modifications in the team arising from mechanical failure. Simulations of a cooperative collection task validate the effectiveness of the presented method.

An adaptive task assignment method for multiple mobile robots via swarm intelligence approach

https://isiarticles.com/bundles/Article/pre/pdf/15590.pdf

Dandan Zhang

Papers

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

Fuzzy Approximation-Based Task-Space Control of Robot Manipulators With Remote Center of Motion Constraint

Coordinated Transport by Multiple Biomimetic Robotic Fish in Underwater Environment

An adaptive task assignment method for multiple mobile robots via swarm intelligence approach

Geometric topology based cooperation for multiple robots in adversarial environments