Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Abstract

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

Visualization Techniques via MLBS for Personnel Management in Major Events

“…The developments and applications of MLBSs emerge one after another, vehicle tracking, campus navigation, consumer behavior analysis, take-out food, beach positioning, location recognition, online map, intelligent transportation, teammate real-time location sharing, etc. [12][13][14][15]. Visualization in a system based on MLBS is the graphical representation of personnel information and activity functions to help users intuitively understand the meanings of big data and simplify operations by using charts, plots, maps, videos, and more [16] So far, there are many types of methods, tools, and platforms for data visualization made simple, including ECharts [17,18], D3.js, Zoho analytics, Tableau Infogram, ChartBlocks, and so on [19,20].…”

Quasi-Reflective Chaotic Mutant Whale Swarm Optimization Fused with Operators of Fish Aggregating Device

“…To address the above drawbacks and to improve the performance of the algorithm, on the one hand, domestic and foreign researchers have proposed many relevant improved WOA algorithms by using many strategies, such as changing operators [9][10][11][12], fusing chaotic mappings [13][14][15], and mixing other algorithms with the WOA algorithm [16][17][18][19]. For example, a Cauchy mutator was introduced into WOA to vary the individual movement step of whales through the Cauchy inverse cumulative distribution function method [9]; the combination of lens imaging backward learning and optimal worst backward learning strategies were used to improve the quality of swarm individuals [10].…”

“…In addition, mixing other algorithms is a common strategy in WOA improvement, and combining local search strategies, it can effectively reduce the occurrence of WOA algorithms falling into local optimum situations. For example, by mixing with algorithms, such as slime mould algorithm (SMA) [16], social group optimization (SGO) [17], teachinglearning-based optimization (TLBO) [18], particle swarm optimization (PSO) [19,20], bat algorithm (BA) [21], and grey wolf optimizer (GWO) [22], it not only reduces the occurrence of falling into local optima, but also solves the problems of insufficient search capability and low efficiency of WOA when high-dimensional problems exist, which is of high use for the performance improvement of WOA algorithms.…”

“…), rand(0, 1) > FADs (17) where CF is the adaptive parameter controlling the movement step of the predator; FADs are the influence probability, taken as 0.2; r is the random number within [0, 1]; x rand1 and x rand2 are the randomly selected individual whale position vectors, respectively. On this basis, to limit the FADs to perform the search blindly in the early stage, which affects the convergence of the algorithm, this paper only enables the FADs in the late stage of the algorithm iteration and strengthens the upfront capability of the algorithm by introducing the variation operation.…”