A Bio-Inspired Neural Network Approach to Robot Navigation and Mapping with Nature-Inspired Algorithms

Bio-inspired algorithms have been increasingly applied for autonomous robot path planning problems in complex environments. These environments are often restrictive in nature, where robot navigation must succeed with low margins of error. The complexity of the environment is a performance limiting factor based on density of obstacles and navigability of the robot in difficult environments. The scale of the environment to be examined for any given problem also contributes to the performance of solutions for path planning. These performance limitations are especially evident in time sensitive real-world applications, like autonomous off-road vehicles or search and rescue situations, where computation quality and immediacy are highly valued. One method to mitigate the shortcomings of bio-inspired algorithms involves destructing the problem environment into readily solvable segments. This paper proposes a graph-based near optimal path approach leveraging a bio-inspired algorithm for rapid path planning in autonomous environments. The proposed model utilizes centroid cell decomposition to establish a map in complex environments in a graph-based form. In this approach, centroid points are regulated and determined by the bio-inspired optimization as part of generating final robot trajectories. To improve upon the shortcomings of typical graph-based algorithms, ant colony optimization is applied afterwards to determine the near optimal robot traversal path. The model is validated through simulated environments for performance with comparable algorithms.

Section: Introductionmentioning

confidence: 99%

Centroid-based cell decomposition robot path planning algorithm integrated with a bio-inspired approach

Rogers,

Sellers,

Lei

et al. 2024

“…Recent developments in the field have increasingly focused on creating adaptive and integrated systems that can navigate autonomously with greater intelligence and efficiency. The integration of mapping, path planning, and optimization algorithms, as seen in the works of Lei et al, 22 , 23 , 24 represents a significant stride towards achieving this goal. Furthermore, the application of bio-inspired algorithms and machine learning techniques in path planning, exemplified by Sellers et al, 25 , 26 Short et al, 27 and Jayaraman et al, 28 highlights the field's move towards leveraging computational intelligence to solve complex navigation problems.…”

Section: Introductionmentioning

confidence: 99%

Synergizing graph-based methods with biologically inspired algorithms for enhanced robot path planning efficiency

Sellers,

Lei,

Carruth

et al. 2024

Robotic path planning and navigation in intricate environments pose significant challenges in various domains, including search and rescue, agriculture, and various defense applications. There have been various methods proposed to solve these problems, such as graph-based methodologies. Although the majority of cell decomposition methods lack the capability to develop a near optimal path, we propose a middle point cell decomposition in combination with a biologically inspired optimization algorithm for robot path planning and mapping. The proposed model leverages vertical cell decomposition in combination with an enhanced biologically inspired particle swarm optimization algorithm (ePSO). Vertical cell decomposition is employed as a spatial partitioning technique, segmenting complex environments into vertical cells, each characterized by a simplified geometric representation. To improve the path finding process, we introduce middle points within these cells. In this research, midpoints in the graph are regulated and slid by the developed biologically inspired optimization approach to generate optimal robot trajectories. This method enables the algorithm to approximate complex geometry more accurately and efficiently, facilitating smoother navigation for robotic systems. The primary objective of this study is to develop a comprehensive model for robotic path planning and navigation in complex environments, with a particular focus on enhancing efficiency, adaptability, and robustness. The proposed model is validated through extensive simulations in diverse complex environments. Comparative studies are performed against existing path planning algorithms, demonstrating the effectiveness of our approach in terms of path quality, computational efficiency, and adaptability to changing conditions

“…11,12 To effectively deploy robotic systems in real-world applications, the development of autonomous robot multi-waypoint navigation and mapping systems is crucial. 13 A plethora of algorithms has been devised to tackle the autonomous robot navigation challenge, including multi-task planning algorithms, 10 ant colony optimization (ACO), 14,15 graphbased methods, 16,17 neural networks, [18][19][20][21] natural inspired algorithms, 8 fuzzy logic, 22,23 artificial potential fields (APF), 24 sampling-based strategies, 25,26 etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot path planning using potential field-based simulated annealing approach

Alarabi,

Lei,

Santora

et al. 2024

The rapid growth of robot applications across various sectors, such as agriculture, disaster management, and package delivery, has led to an increased demand for efficient and safe multi-robot path planning methods. Existing solutions face significant challenges in simultaneously planning paths for multiple robots, maintaining safe distances from obstacles and other robots, and dealing with local minima issues. In this research, an efficient potential field-based method with nature-inspired algorithm is proposed to overcome these limitations, resulting in competent, short, and time-saving paths even in complex environments. Our proposed algorithms consider the simultaneous path planning of robots located at different locations while ensuring a safe distance from other robots. Multi-Robot multi-task allocation method is developed to optimize the path distance and time. A potential field-based scheme is proposed to avoid static and dynamic obstacles effectively. Additionally, our approach solves the local minima problem with simulated annealing algorithm, which is a supplement to this navigator. Finally, the proposed approach is efficient in generating short and time-efficient paths, even in complex dynamic environments. By augmenting the efficiency and safety of multi-robot operations, our work will contribute to the reduction of operational expenses, the enhancement of productivity, and the minimization of accident risks.