Towards Smarter Swarms: Optimizing Search Patterns and
Exploration with AI-Driven Frameworks
Monday December 16th at 14:00
Wolfson Building of Mechanical Engineering, Room 206
Abstract:
This research investigates the performance and efficiency of multi-agents in multi-target tracking scenarios using the Adaptive Particle Swarm Optimization with k-Nearest Neighbors (APSO-kNN) algorithm. The study explores various search patterns-Random Walk, Spiral, Lawnmower, and Cluster Search to assess their effectiveness in dynamic environments. Through extensive simulations, we evaluate the impact of different search strategies, varying the number of targets and agent’s sensing capabilities, and integrating a Pursuit-Evasion model to test adaptability. Our findings demonstrate that systematic search patterns like Spiral and Lawnmower provide superior coverage and tracking accuracy, making them ideal for thorough area exploration. In contrast, the Random Walk pattern, while highly adaptable, shows lower accuracy due to its non-deterministic nature, and Cluster Search maintains group cohesion but is heavily dependent on target distribution. The mixed strategy, combining multiple patterns, offers robust performance across varied scenarios, while APSO-kNN effectively balances exploration and exploitation, making it a promising approach for real-world applications such as surveillance, search and rescue, and environmental monitoring. This study provides valuable insights into optimizing search strategies and sensing configurations for swarms, ultimately enhancing their operational efficiency and success in complex environments.
On the second part of this talk, we address the challenge of exploring unknown indoor environments using autonomous aerial robots with Size Weight and Power (SWaP) constraints. The SWaP constraints induce limits on mission time requiring efficiency in exploration. We present a novel exploration framework that uses Deep Learning (DL) to predict the most likely indoor map given the previous observations, and Deep Reinforcement Learning (DRL) for exploration, designed to run on modern SWaP constraints neural processors. The DL-based map predictor provides a prediction of the occupancy of the unseen environment while the DRL-based planner determines the best navigation goals that can be safely reached to provide the most information. The two modules are tightly coupled and run onboard allowing the vehicle to safely map an unknown environment. Extensive experimental and simulation results show that our approach surpasses state-of-the-art methods by 50-60% in efficiency, which we measure by the fraction of the explored space as a function of the trajectory length.
Bio:
Oren received my B.Sc. in Aerospace Engineering, M.Sc. degree in Mechanical Engineering and a Ph.D. in Geo-information Engineering, all from the Technion – Israel Institute of Technology. Oren is currently an Assistant Professor, heading the Swarm and AI (SAIL) Lab, at the Hatter Department of Marine Technologies. Prior to joining the University of Haifa, Oren was the founder and CTO of Autonomy & Data Science R&D in the Israeli Navy (CDR Ret.) and DDR&D for twenty years, working with research partners around the world and leading research groups (DARPA, ARL, NRL, ONR etc). In the last ten years, he is working on joint research with CSAIL & LIDS MIT labs and with Marine Robotics Lab at MIT, UPenn, all on swarms and machine learning algorithms.
Oren’s research focus on swarms and AI across scales. The adaptability and scalability of swarms make them particularly suited to tasks that require distributed sensing, acting, and processing, presenting numerous possibilities for addressing complex and large-scale challenges facing humanity. From nanorobots for cancer treatment, to environmental monitoring and conservation in the ocean, or disaster response and recovery, traffic management and logistics etc. - swarms, particularly swarm intelligence of AUVs, USVs and drones.
In our cutting-edge research lab, Oren’s research delve into the complex and rapidly evolving field of swarms and autonomy, leveraging the latest advancements in Artificial Intelligence (AI) to push the boundaries of autonomous systems across scales.
