Hokkaido University · Engineering
Professor Ankit A. Ravankar's research lab specializes in autonomous mobile robotics, with a focus on intelligent navigation, mapping, and multi-robot coordination in complex and dynamic environments. The lab develops advanced sampling-based path planning, hybrid metric-topological mapping, and knowledge-sharing frameworks to enable safe and efficient robot operation in real-world settings such as vineyards, large indoor facilities, and aged-care environments. Emphasis is placed on robustness in GPS-denied and cluttered environments using sensor data (e.g., LiDAR), while minimizing computational cost and enabling real-time performance. The lab also pioneers multi-robot cooperation systems that enhance situational awareness and operational efficiency through semantic-aware, distributed information sharing.
Figures are computed from collected data and may differ slightly.
Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments. Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications. However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph. The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performan
Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the rob
In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an a
Map generation by a robot in a cluttered and noisy environment is an important problem in autonomous robot navigation. This paper presents algorithms and a framework to generate 2D line maps from laser range sensor data using clustering in spatial (Euclidean) and Hough domains in noisy environments. The contributions of the paper are: (1) it shows the applicability of density-based clustering methods and mathematical morphological techniques generally used in image processing for noise removal f
Demographic changes in our society are putting a heavy burden on care facilities and health-care infrastructure. While the elderly population is steadily increasing, there is an acute shortage of caregiving experts and professionals. This problem is becoming more severe in superaging societies, namely, Japan. Hence, this urges new and practical solutions for welfare facilities to mitigate the burden on caregivers and human supporting partners by introducing robotics assistance through informatio
In this paper, we present a hybrid topological mapping and navigation method for mobile robots. The proposed method combines metric and topological information to create map and generate navigation plan for the robot. As compared to traditional approaches of robot mapping, the method is lightweight and can be used for mapping and navigation in large areas which is particularly useful for service robots operating in large buildings. The method only uses local information for navigation while main
Mapping and exploration are important tasks of mobile robots for various applications such as search and rescue, inspection, and surveillance. Unmanned aerial vehicles (UAVs) are more suited for such tasks because they have a large field of view compared to ground robots. Autonomous operation of UAVs is desirable for exploration in unknown environments. In such environments, the UAV must make a map of the environment and simultaneously localize itself in it which is commonly known as the SLAM (s
This paper presents the results of using mixed clustering (k-means and DBSCAN clustering) with singular value decomposition to build map in a noisy environment from laser range sensor information. Sensors are prone to errors, moreover, environmental and other factors may affect the sensor sensitivity. This may generate a lot of noise which must be removed before building the map. The study shows how density based clustering techniques can, without losing critical information, greatly reduce nois
The present university education system has been designed to make graduate students good problem solvers such that they can contribute to society through the skills acquired in the graduate schools. With focus on developing critical thinking and reasoning to solve local and global problems such programs have gained immense popularity among teachers and professors in schools and universities and termed as "Problem-Based-Learning"" (PBL). However, there has been very few programs that encourage st
The current graduate school education system has largely been focusing on producing better learners and problem solvers. The rise of problem based learning approaches are testimonial to the importance of such skills at all levels of education from early childhood to graduate school level. However, most of the programs so far have focused primarily on producing better problem solvers neglecting problem finding at large. Problem finding, an important skill is a subset and first step in creative pr
Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map,
In this paper we discuss the map building technique for mobile robot using k -means clustering using laser ranger sensor. Clustering is an efficient technique to group together data set to obtain accurate maps for autonomous mobile robots. We discuss the k -means clustering algorithm in detail with experimental results and how this method can be used to obtain straight line maps for indoor environments. We discuss our results with different sizes of clusters and for data set with noise. Results
Autonomous indoor service robots use the same passages which are used by people for navigation to specific areas. These robots are equipped with visual sensors, laser or sonar based range estimation sensors to avoid collision with obstacles, people, and other moving robots. However, these sensors have a limited range and are often installed at a lower height (mostly near the robot base) which limits the detection of far-off obstacles. In addition, these sensors are positioned to see forward, and
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