Candidate: Megnath Ramesh
Date: August 5, 2025
Time: 1:00 PM
Location: Online - contact the candidate for more information.
Supervisors: Dr. Stephen Smith, Dr. Baris Fidan
Abstract:
We study the problem of planning near-optimal coverage paths for a robot operating in an environment such that a sensor or tool attached to the robot covers the environment. We consider environments that contain unknown obstacles that are detected by the robot during the execution of the coverage path. Such obstacles necessitate a replan of the coverage path that safely avoids the new obstacles while still minimizing a cost function (coverage time, length, and/or turns).
For an environment with known obstacles, existing coverage planning approaches discourage turns in the path by covering the environment along the least number of coverage lines, i.e., straight-line paths. This is because turns not only slow down the robot but also negatively affect the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. However, existing approaches do not guarantee the minimum number of coverage lines. To this end, we propose a minimum-turn coverage planning approach, namely optimal axis-parallel rank partitioning (OARP), that guarantees coverage along the minimum number of axis-parallel (horizontal/vertical) coverage lines. Using simulations in real-world environments, we show that OARP improves upon state-of-the-art approaches in turns and coverage time.
When there are unknown obstacles in the environment, recomputing such paths online are computationally expensive. In such cases, the robot may have to wait for a safe path to continue coverage, resulting in robot stoppages that increase coverage time. To enable replanning with minimal stoppage, we extend OARP to introduce an anytime coverage replanning approach. Upon detecting a new obstacle in the environment, we replan an initial coverage path obtained from OARP within a given time budget (e.g., time to reach the new obstacle). Given this budget, the replanning approach also aims to minimize the overall path turns and coverage time. We showcase the replanning framework in experiments using an industrial cleaning robot avoiding unknown obstacles.
We then study two problems towards improving upon the above framework. First, we analyse the length of the coverage paths returned by OARP and obtain an approximation guarantee that quantifies the sub-optimality of OARP path lengths. Following this, we propose an approach that improves upon this approximation factor and computes shorter coverage paths in practice than OARP. The second problem is to remove the axis-parallel constraint imposed by OARP on the coverage lines. We propose decomposing the environment into sectors, i.e., possibly overlapping rectangular sub-regions that can each be covered by straight-line paths parallel to the longest sector edge. Using results from submodular set cover (SSC) problems, we propose a greedy approach to compute sectors and provide an approximation guarantee on the number of sectors in the decomposition.