Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

Abstract

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.

Robot obstacle avoidance optimization by A* and DWA fusion algorithm

“…The improved A* can improve path planning in the static environment. However, in situations where there are uncertain and dynamic obstacles during the planning process, if the mobile robot continues to follow globally planned path, it will be unable to avoid these obstacles [ 18 ]. Therefore, this research will introduce DWA-based path planning to optimize robot mobile obstacle avoidance, and optimize local path planning effect of algorithm by improving DWA, so that robot can better achieve dynamic obstacle avoidance.…”

Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm

“…Unmanned aerial vehicles (UAVs) are widely used and important in our lives because of their ability to operate in challenging and dangerous areas, particularly in the military and industrial sectors. Planning a route from a starting point to a target location while avoiding environmental obstacles is critical to the mission's success [1], [2]. Path planning is generally divided into two types: when the environment is known, path planning is said to be offline, and when it can adjust a path in reaction to environmental changes, it is said to be online [3], [4].…”

“…On the other hand, five categories have been established for UAV 3D route planning algorithms. Each category differs from the others in terms of compatibility with specific characteristics [1]- [5]. The first category is node-based algorithms, including the A*, dijkstra algorithms [6], [7], and the D-star algorithm [8].…”

Planning the Optimal 3D Quadcopter Trajectory Using a Delivery System-Based Hybrid Algorithm

“…A hybrid (HFAMCPSO) method that combines the modified chaotic particle swarm optimization (MCPSO) and firefly algorithm (FA) is suggested by the authors in reference [25]. The hybridization improved the variety of options and helped prevent stagnation.…”