Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

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 robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

Measuring Student Learning Outcomes in Introductory Project Management Course in Graduate Schools

In this paper, we discuss the learning outcomes of the graduate students in an introductory project management course. Our study utilizes survey results to determine the student's learning outcomes in terms of their cognitive thinking, critical thinking, perception, and fundamental knowledge of management studies in a trans-graduate school. We also evaluated the improvement in the student's interpersonal skills and motivation after the course. We present the methods and discuss the essential need to introduce project management practices at an early stage in graduate schools and critical dimensions of student learning experiences using pre-course and post-course survey results. The survey measures the student's ability to grasp project management core concepts and practices, and attitude towards project management in general. From our studies, several key research conclusions have been drawn with respect to the pedagogical methods. The study suggests that with a cumulative and an immersive course syllabus that involves hands-on experience, and exposure to essential concepts of project management techniques, student's self-confidence and ability to reason and handle new projects significantly i mproves. The course framework is based on problem-based learning with mixed student interactions from different backgrounds and diverse nationalities that improved their problem-solving abilities and ability to work in a team environment. The study also discusses the impact of early introduction to project management techniques and student's employability

Intelligent Robot Guidance in Fixed External Camera Network for Navigation in Crowded and Narrow Passages

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 robot is often ’blind’ about objects (ex. people and robots) moving behind the robot which increases the chances of collision. In places like warehouses, the passages are often narrow which can cause deadlocks. We propose to use a network of external cameras fixed on the ceiling (ex. surveillance cameras) to guide the robots by informing about moving obstacles from behind and far-off regions. This enables the robot to have a ’birds-eye view’ of the navigation space which enables it to take decisions in real-time to avoid the obstacles efficiently. The camera sensor network is also able to notify the robots about moving obstacles around blind-turns. A mutex based resource sharing scheme in camera sensor network is proposed which allows multiple robots to intelligently share narrow passages through which only one of the robots/person can pass at a given time. Experimental results in simulation and real scenarios show that the proposed method is effective in robot navigation in crowded and narrow passages

室内環境におけるマルチロボット経路計画、タスク協調、環境地図構築及び自己位置同定のアルゴリズム開発

Indoor mobile service robots face many different problems to perform tasks au-tonomously. These problems become more complex when multiple robots are deployed in the environment for different services. This thesis addresses four important problemsfaced by multiple autonomous mobile robots in indoor environments.First, is the problem of smooth path generation for multiple mobile robots. Careful path planning is necessary for autonomous robots to navigate from their current position to the service location. A smooth and continuous path is desired for robot motion which avoids abrupt and sharp turns. This problem is further complicated in case of multiple robots as two or more robots may have intersecting and common paths which increases the chances of a robot colliding with another robot during its operation. Mul-tiple robots in the environment also poses problems of deadlock which must be resolved.The second problem which frequently occurs in multi-robots scenarios is the problem of common resource (like charging point or narrow path) sharing. Service robots for cleaning and patrolling are mostly in continuous operation and require frequent recharging. Using a large number of docking points proportional to the number of robots is costly and requires large space. A manager which can resolve conflicts and allocate the resource to the most appropriate robot by considering several factors like task priority, battery power left, distance travelled, etc. is required.Third, is the problem of autonomous task coordination and exploration by multiple mobile service robots. The problem is related to but more abstract than the first problem of smooth path generation, in which, the goal locations are given to each mobile robot. However, introducing multiple robots also introduce the problem of programming the robots to efficiently serve the region. The areas to serve in a map may vary with time. Moreover, the number of the robots available to serve may also be dynamic, in real world situations, as some of the robots may be charging, while some may be out of order. In case of robots used for surveillance, a robot may want other robot or robots to follow itself while chasing a suspicious person, for backup. This situation is also dynamic in terms of availability of robots, selecting the nearest robot for quick response, and selecting the same path towards a particular area as taken by the previous robot. In both the cases, explicitly programming the robots is cumbersome, and demands for a simpler scheme in which multiple robots can intelligently coordinate the tasks and explore the map. Moreover, if multiple robots are serving the same area, or navigating to the same area, they must coordinate tasks to maximize efficiency.The fourth problem is related to the third problem and relates to virtual pheromone deposition methodologies for multi-robot task coordination. Most of these approaches assumes pheromone deposition at perfect locations in the map. In reality however, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot, and the dynamics of the environment in which the robot operates. In real world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a ‘blind leading the blind’ scenario which reduces the efficiency of the multi-robot system.The final problem is a fundamental problem of Simultaneous Localization and Mapping (SLAM) in autonomous mobile robots. Feature based SLAM algorithm are popular which work by matching features like lines extracted from the sensors like camera or laser range sensors (LRS). Particularly, line detection is an important problem in computer vision, graphics, and autonomous robot navigation. However, in order to achieve efficiency in path planning, path smoothing, task distribution and other tasks, a robust SLAM algorithm is indispensable

Avoiding blind leading the blind: Uncertainty integration in virtual pheromone deposition by robots

Virtual pheromone trailing has successfully been demonstrated for navigation of multiple robots to achieve a collective goal. Many previous works use a pheromone deposition scheme that assumes perfect localization of the robot, in which, robots precisely know their location in the map. Therefore, pheromones are always assumed to be deposited at the desired place. However, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot and the dynamics of the environment in which the robot operates. In real-world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a "blind leading the blind'' scenario that reduces the efficiency of the multi-robot system. We propose a pheromone deposition algorithm, which models the uncertainty of the robot's pose. We demonstrate, through experiments in both simulated and real environments, that modeling the uncertainty in pheromone deposition is crucial, and that the proposed algorithm can model the uncertainty well

On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps

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, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the ‘correspondence problem’ in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a ‘node-map’ with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle’s positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed

On a bio-inspired hybrid pheromone signalling for efficient map exploration of multiple mobile service robots

This paper presents a novel bio-inspired hybrid communication framework that incorporates the repelling behaviour of anti-aphrodisiac pheromones and attractive behaviour of pheromones for efficient map exploration of multiple mobile service robots. The proposed communication framework presents a scheme for robots to efficiently serve large areas of map, while cooperating with each other through proper pheromone deposition. This eliminates the need of explicitly programming each service robot to serve particular areas of the map. The paths taken by robots are represented as nodes across which pheromones are deposited. This reduces the search space for tracking pheromones and reduces data size to be communicated between robots. A novel pheromone deposition model is presented which takes into account the uncertainty in the robot's position. This eliminates robots to deposit pheromones at wrong places when localization fails. The framework also integrates the pheromone signalling mechanism in landmark-based Extended Kalman Filter (EKF) localization and allows the robots to capture areas or sub-areas of the map, to improve the localization. A scheme to resolve conflicts through local communication is presented. We discuss, through experimental and simulation results, two cases of floor cleaning task, and surveillance task, performed by multiple robots. Results show that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming

SHP: Smooth Hypocycloidal Paths with Collision-Free and Decoupled Multi-Robot Path Planning

Generating smooth and continuous paths for robots with collision avoidance, which avoid sharp turns, is an important problem in the context of autonomous robot navigation. This paper presents novel smooth hypocycloidal paths (SHP) for robot motion. It is integrated with collision-free and decoupled multi-robot path planning. An SHP diffuses (i.e., moves points along segments) the points of sharp turns in the global path of the map into nodes, which are used to generate smooth hypocycloidal curves that maintain a safe clearance in relation to the obstacles. These nodes are also used as safe points of retreat to avoid collision with other robots. The novel contributions of this work are as follows: (1) The proposed work is the first use of hypocycloid geometry to produce smooth and continuous paths for robot motion. A mathematical analysis of SHP generation in various scenarios is discussed. (2) The proposed work is also the first to consider the case of smooth and collision-free path generation for a load carrying robot. (3) Traditionally, path smoothing and collision avoidance have been addressed as separate problems. This work proposes integrated and decoupled collision-free multi-robot path planning. ‵Node caching‵ is proposed to improve efficiency. A decoupled approach with local communication enables the paths of robots to be dynamically changed. (4) A novel ‵multi-robot map update‵ in case of dynamic obstacles in the map is proposed, such that robots update other robots about the positions of dynamic obstacles in the map. A timestamp feature ensures that all the robots have the most updated map. Comparison between SHP and other path smoothing techniques and experimental results in real environments confirm that SHP can generate smooth paths for robots and avoid collision with other robots through local communication