Real-time path planning for a swarm of autonomous systems
[thesis]
Sumana Biswas
2019
This thesis contributes to the growing research of multi-agent autonomous systems concerned with the real-time planning. Over the years, autonomous systems consisting of mobile agents have proven to be efficient, robust and versatile tools for exploration (e.g. space robots), military (e.g. for search and rescue operations), and industrial applications (e.g. Google self-driving cars). With autonomous technologies getting matured day by day, deploying multiple autonomous agents to complete
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... x tasks are getting lot of interests for many different applications. If a single autonomous agent can complete a job, multiple autonomous agents could potentially complete the job faster. However, introducing multiple agents make the overall system more complex since agents now need to be capable of collaborating with each other effectively. Randomly introducing autonomous agents without an effective mechanism for collaboration might negatively impact the productivity. This thesis is motivated by the goal of making multi-agent autonomous system ubiquitous for real-world applications. We have taken a bottom-up approach in developing algorithmic machineries to address the challenges on our way to satiate that goal. For a mobile agent operating in a dynamic environment, the success of executing a task hinges on how effectively it can navigate to the target location. Path planning becomes even more demanding if we introduce more autonomous agents in the environment since the agents now have to treat each other as dynamic obstacles. The path planning algorithm not only needs to avoid obstacles but also need to be fast enough to re-plan if the mobile agent encounters unexpected obstacles during navigation. Moreover, the path planning algorithm needs to guarantee that the agent can traverse the path while satisfying its mechanical constraints. A Simultaneous Replanning Vectorized Particle Swarm Optimization (SRVPSO) algorithm based on stochastic optimization is developed to find out an optimal cost path by avoiding static and dy [...]
doi:10.26190/unsworks/21591
fatcat:3pha7h4vkze5vgmg5hbwdevvii