Dr Awadhesh Dixit, Assistant Professor, Department of Computer Science and Engineering published a paper on Bio-inspired optimal path planning for UAVs with obstacle avoidance and energy efficiency in the journal Cluster Computing. The research focuses on developing an intelligent path-planning system for Flying Ad Hoc Networks (FANETs)—groups of drones that communicate without fixed infrastructure. Read the paper to gain interesting insights.
Brief Abstract
Achieving an efficient and collision-free path remains a critical challenge for unmanned aerial vehicles (UAVs) due to their high mobility, dynamic environments, and energy constraints. This paper proposes a novel hybrid path-planning model that integrates Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to address these challenges. PSO is used to identify energy-efficient, destination-aware paths, while GWO enhances obstacle avoidance and threat adaptability in real time. The model is evaluated using metrics such as path optimality, energy consumption, robustness, time efficiency, and collision avoidance. Simulation results in MATLAB demonstrate that the proposed PSO-GWO model outperforms existing methods, producing shorter, safer, and more cost-efficient paths. Additionally, statistical validation through ANOVA confirms the model’s superiority in optimizing key performance indicators.
Explanation in Layperson’s Terms.
Flying Ad Hoc Networks (FANETs) are communication networks formed by drones that can connect and coordinate with each other without needing fixed infrastructure like cell towers. These networks allow groups of drones to share information in real time while flying. For drones to successfully complete their missions, one of the most important abilities they need is smart path planning — deciding the best route to fly.
The research focuses on helping drones fly in a smarter, safer, and more energy-efficient way. In the real world, a drone flying from one place to another cannot move in a straight line because there are many obstacles such as buildings, trees, electric wires, and even moving objects. At the same time, drones have limited battery power, so they must choose their routes carefully. In this work, nature inspired hybrid technique —such as how birds avoid obstacles while flying or how ants find the shortest path to food—to design an intelligent path-planning system. This system allows a drone to automatically select the best possible route that avoids collisions, reduces unnecessary movement, and saves battery energy. As a result, the drone can fly longer distances, complete its tasks more efficiently, and operate more safely. Such smart navigation is especially useful in real-life applications like delivering medical supplies, searching for survivors during disasters, monitoring crops in agriculture, and inspecting infrastructure, where safe and energy-aware drone operation can make a significant difference.
Practical Implementation and Social Implications
The proposed bio-inspired energy-aware path planning framework has strong practical relevance in disaster response, medical logistics, precision agriculture, smart surveillance, and infrastructure inspection. By enabling safe, collision-free, and energy-efficient UAV navigation, the system enhances mission endurance while reducing operational risk. However, its deployment must consider privacy, ethical use, and regulatory compliance to ensure socially responsible adoption.
Collaborations
Co-Author: Sunil Kumar Singh, Associate Professor
Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
Future Research Plans
Integrating the model with machine learning techniques for predictive obstacle mapping and energy-sensitive flight strategies could further enhance its intelligence and responsiveness. We can also Test and validate in real time application such as emergency response, environmental monitoring.
Link to the Article
https://doi.org/10.1007/s10586-025-05876-y