Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

Abstract

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

“…In [61], A* and potential field [67], A* and reinforcement learning [63], A* and the Dynamic Window Algorithm [66], Theta* and dipole field with the dynamic window approach [64] are explored. Other studies concentrated on merging various optimization strategies to address the complexities in path planning, for instance, integrating the Wolf Swarm Algorithm with the artificial potential field (WSA-APF) [62], the kidneyinspired algorithm and Sine-Cosine Algorithm (KA-SCA) [70], Artificial Bee Colony and Evolutionary Programming (ABC-EP) [71], the Modified Hyperbolic Gravitational Search Algorithm and Dynamic Window Approach (MGSA-DWA) [72], the Self-Organizing Migrating Algorithm and Particle Swarm Optimization (SOMA-PSO) [73], the Grey Wolf Optimizer and Whale Optimizer Algorithm (GWO-WOA) [69], and the Dynamic Window Approach (DWA) and Teaching-Learning-Based Optimization (TLBO) [68]. Finally, the combination of sampling-based and optimization algorithms was employed in [65] in order to solve the path planning problem.…”

“…Additionally, the variation in obstacle shapes is not addressed in most of the existing literature. Finally, only a few studies propose a single dynamic target, such as [50,60,62,77]. Furthermore, managing multiple dynamic targets will present a greater challenge.…”

“…UGV [36,37,39], [41,45,57], [62,69,71], [47,59,61], [66,67,70], [73] Centralized Humanoid [64,72] -Single point of failure.…”

A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks

“…Abdulhussein et al [40] created a reliable cascade P-PI controller to regulate the speed and position of the permanent magnet DC motor. Hamed et al [41] suggested a cooperative hunting approach for a multi-robot system based on the wolf swarm algorithm whose behaviour is unforeseen by numerous robots. Methods were also presented that combined universal, problem-free swarm intelligence algorithms with simple deterministic domain-specific heuristic algorithms [42], [43].…”

EEG-Based Empathic Safe Cobot

“…Collaborative robots (cobot) [1] are special robots that can collaborate with humans. They were originally intended for industrial applications [2][3][4][5][6][7][8][9], but they have also found use in biomedical [10][11][12][13][14][15][16][17][18], domestic [19][20][21][22], and military [23][24][25][26] fields.…”