2021/07/06 by Andreas Orthey, Orthey, Andreas, Florian T. Pokorny +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #FOS: Mathematics #Genome Rearrangement Algorithms #Metaheuristic Optimization Algorithms Research #Optimization and Control (math.OC) #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2107.02498
openalex publication_date 2021/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this extended abstract, we report on ongoing work towards an approximate multimodal optimization algorithm with asymptotic guarantees. Multimodal optimization is the problem of finding all local optimal solutions (modes) to a path optimization problem. This is important to compress path databases, as contingencies for replanning and as source of symbolic representations. Following ideas from Morse theory, we define modes as paths invariant under optimization of a cost functional. We develop a multi-mode estimation algorithm which approximately finds all modes of a given motion optimization problem and asymptotically converges. This is made possible by integrating sparse roadmaps with an existing single-mode optimization algorithm. Initial evaluation results show the multi-mode estimation algorithm as a promising direction to study path spaces from a topological point of view.