Optimal Path Planning for Aerial Swarm in Area Exploration
Time: Mon 2022-06-27 14.00 - 15.00
Location: Harry Nyquist MV10, floor 7
Video link: https://kth-se.zoom.us/j/63158669637
Language: English
Respondent: Johanna Norén , Reglerteknik/DCS
Opponent: Victor Nan Fernandez-Ayala
Supervisor: Johan Markdahl (FOI), Pian Yu
Examiner: Dimos Dimarogonas
Abstract: This thesis presents an approach to solve an optimal path planning problem for a swarm of drones. We optimize and improve information retrieval in area exploration within applications such a ‘Search and Rescue’-missions or reconnaissance missions. For this, dynamic programming has been used as a solving approach for a optimization problem. Different scenarios have been examined for two types of system, a single-agent system and a multiagent system. First, there have been restrictions on the agents movement in a grid map and for that, optimal paths have been computed for both systems. Thereafter, two different solving approaches within dynamic programming have been tested and compared. The greedy approach which is a standard use where each agent computes the most optimal path from its own perspective and a simultaneous solving approach where the agents compute the most optimal paths according to all agents perspective. The simultaneous solving approach performed better than the greedy approach, which was expected since it is a more swarm optimal approach. However, it has a higher computational complexity which grows exponentially unlike to the greedy approach. Lastly, we discuss the case when the agents are allowed to move in all directions to optimize the information retrieval for the swarm. Here, dynamic programming turns out to have limitations for our use and purpose. For future work, a suggestion is to model the problem with multiple objective functions instead of one as has been done in this thesis. Also, it would be interesting trying another solving method for the problem. To this, I give example of two methods that would be interesting to compare, using model predictive control or a machine learning-based solution such as reinforcement learning.