Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization

Abstract

Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner. INDEX TERMS Path planning, unmanned vehicles, fuzzy logic (FL), ant colony optimization (ACO).

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

“…First, it would be interesting to investigate if the application of the pseudo-random action choice rule [20] could improve the MS-MMAS results. Another further promising idea is the use of pheromone update rule based on ants ranking [25]. Extension of the MS-MMAS implementation design with parallel computing techniques [10] and hybridization with other meta-heuristics [26][27][28] is other interesting opportunity for the future research.…”

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

“…In this sense, Artificial Intelligence (AI) techniques such as Fuzzy Logic (FL), Neural Networks (NNs) and meta-heuristics are recurrently found in the specialized literature. In [19], the Ant Colony Optimization (ACO) algorithm improved with FL is used to obtain cost-effective paths with an unmanned aerial vehicle in dynamic environments. Another example is the work in [20], where a NN is used to dynamically predict and avoid collisions in the path-planning for mobile robots.…”

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

“…Virtual map and the real map Accelerated convergence speed [12] Dynamic Fuzzy logic based path planning Wireless sensor networks in MATLAB Localization ratio and localization accuracy [13] Dynamic fuzzy-logic-ant colony system Regions of London, United Kingdom Efficient route selection [14] Ant colony and fuzzy logic Simulated maps in MATLAB Shortest path in minimum time [15] Fuzzy logic ant colony optimization Simulated road networks Shortest path length [16] Cuckoo optimization algorithm Simulated scenarios of size 20 × 20, 100 × 100 and 200 × 200 Safe, smooth, and collision-free path [17] A visual-inertial navigation system Urban areas of Hong Kong Effective mitigation of dynamic objects and improved accuracy [18] Fuzzy-genetic algorithm (GA) with three path concept Simulated maps Computationally efficient [19] Improved gravitational search Real-time navigation using Khepera III mobile robot The safe and shortest path [20] Genetic Zhang et al [25] made an extensive survey on path planning approaches for mobile robots. In their work, they emphasized the advantages of using a genetic algorithm, particle swarm optimization, ant colony optimization, and artificial potential fields in path planning with future directions.…”

“…In this stage, the pheromone update rule is used to find the current pheromone quantity. The intensity of pheromone is updated using (15). The usability of the path is either increased by pheromone reinforcement, or decreased through pheromone evaporation.…”