Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm

The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.Comment: Final version as submitted to ICAPS-2017 (main track); 8 pages; 4 figures; 1 algorithm; 2 table

Evaluation of RGB-D SLAM in Large Indoor Environments

Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge, still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.Comment: This is a pre-print of the paper accepted to ICR 2022 conferenc

Modeling and optimizing of business processes : A case with LLC Wim Bosman, Russia

This report was written as a proposal for a Bachelor thesis project. Its objective was to model and optimize the business processes in the Wim Bosman Company in order to standardize the existing template of processes with the solutions for optimization so as to reduce double work and waste of time. The relevance of this study is determined by the fact that modern enterprises are forced to constantly work on improving their operations because of the continually growing competitive landscape. This requires the development of new technologies and methods of conducting business as well as improving the quality of the final results. This also requires the introduction of new, more efficient methods of management and organization in a company. The study concentrated on finding the most efficient processes by using the Business Process Management approach. In this thesis the most important factors, stages and techniques of Business Process Management are discussed. All the employees were separately interviewed in order to collect comprehensive information about the whole process model and to collect suggestions for optimization. Data for the study was also collected by using the relevant literature and the company’s database. As a result of this thesis work the following steps were followed: an existing model was defined, the areas of optimization were discovered and the solutions for optimization were presented and implemented into a new business process model

Prioritized Multi-agent Path Finding for Differential Drive Robots

Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate this issue we suggest a set of modifications to the prominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the most restrictive assumptions (syncronized translation only moves, equal size and speed of the robots) and at providing robustness to the solutions. We evaluate the suggested algorithm in simulation and on differential drive robots in typical lab environment (indoor polygon with external video-based navigation system). The results of the evaluation provide a clear evidence that the algorithm scales well to large number of robots (up to hundreds in simulation) and is able to produce solutions that are safely executed by the robots prone to imperfect trajectory following. The video of the experiments can be found at https://youtu.be/Fer_irn4BG0.Comment: This is a pre-print version of the paper accepted to ECMR 2019 (https://ieeexplore.ieee.org/document/8870957