Path planning and simulation for two aerial robots

Abstract

Aerial robot path planning is a critical aspect of autonomous aerial systems, enabling efficient navigation and obstacle avoidance in complex environments. This paper provides an overview of recent advancements and challenges in aerial robot path planning algorithms. Various techniques, including grid-based methods, sampling-based algorithms, optimization-based approaches, and machine learning-based methods, are discussed. Challenges such as real-time computation, dynamic environments, uncertainty modelling, and scalability are addressed. Additionally, the integration of path planning with perception, localization, and control for seamless operation in real-world scenarios is explored. Future directions for research and potential applications in fields such as surveillance, search and rescue, infrastructure inspection, and environmental monitoring are also discussed. In addition to discussing technical advancements, the paper explores emerging trends in aerial robot path planning, including the utilization of swarm intelligence, cooperative planning strategies, and human-in-the loop approaches. It also considers the implications of regulatory frameworks and safety considerations on path planning algorithms for UAVs. Finally, the paper outlines potential future directions for research in aerial path planning, including the exploration of hybrid approaches, enhanced decision-making algorithms, and the development of standardized benchmarks for performance evaluation. It emphasizes the importance of continued innovation in path planning techniques to support the growing applications of UAVs in fields such as surveillance, mapping, disaster response, and urban air mobility.