Multirobot task allocation based on an improved particle swarm optimization approach

Abstract

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization-based multirobot task allocation method. The performance of the new particle swarm optimization-based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

“…Consequently, Problem (13) is equivalent to Problem (12). Appendix D Property 4: If the UAVs meet the above judgment way, there is at least one solution for the new task assignment problem with this new city.…”

“…The solving methods can be divided into the centralized and distributed algorithms. The centralized algorithms mainly include Genetic Algorithm (GA), Tabu search algorithm, particle swarm optimization algorithm, ant colony algorithm and state-space best-first search algorithm [9][10][11][12][13][14]. Most of these algorithms have the global optimizing capability which can be very helpful in dealing with the small-scale NP-hard problem.…”

An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems

“…Due to the task assignment's natural complexity, it belongs to a type of NP-hard combinatorial optimization problem, which makes the deterministic method unable to solve the problem in polynomial time [1]. Therefore, many heuristics methods have been studied for solving the task assignment problem to generate a suboptimal solution within the accepted time, such as self-organizing map neural network [2], genetic algorithm [3,4], ant colony algorithm [5], and particle swarm optimization [6].…”

“…Constraint (5) ensures that the number of sensors equipped with UAV does not exceed the maximum number of sensors that UAV can be equipped with. Constraint (6) guarantees that the total number of any sensors equipped by the UAV does not exceed the number of sensors available at the base. Constraint ( 7) and ( 8) ensure that each task is only performed once.…”

Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

“…Therefore, exact methods are only suitable for solving the small-sized problems. In order to reduce the difficulty of solving problems caused by the scale of the problem, heuristic methods (e.g., genetic algorithms (GA) [ 12 , 13 , 14 , 15 ], particle swarm optimization [ 16 ], differential evolution algorithm [ 17 ], etc.) have always been an essential solution to the MCMPP and combinatorial optimization problem.…”