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

Preferential Multi-Target Search in Indoor Environments using Semantic SLAM

In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearchComment: 6 pages, 8 figure

An object-oriented navigation strategy for service robots leveraging semantic information

Simultaneous localization and mapping (SLAM) have been an essential requirement for the autonomous operation of mobile robots for over a decade. However, in the wake of recent developments and successes of deep neural networks and machine learning, the conventional task of SLAM is gradually being replaced by Semantic SLAM. Extracting semantic information (such as object information) from sensory data can enable the robot to distinguish different environmental regions beyond the conventional grid assignments of free and occupied. This level of scene awareness is essential for performing higher-level navigation and manipulation tasks and enhancing human-robot interactions. This paper presents an integrated framework that not only builds such maps of indoor environments but also facilitates the execution of ‘Go to object’ tasks with high-level user input. We also present a method to extract meaningful endpoints of navigation based on object class. Our modular stack leverages well-known object detectors (YOLOv3), RGB-D SLAM techniques (RTABMapping) and local navigation planners (TEB) to perform ObjectGoal navigation tasks. We also validate the results of experiments in real environments

Nursing care teaching system based on mixed reality for effective caregiver-patient interaction

In the nursing care tasks such as assistance for transferring and walking, it is necessary to provide appropriate nursing care movements depending on factors such as the patient's pose and the degree of disability. However, for novice caregivers to practice and learn appropriate nursing care, they must practice for a long time under the guidance of skilled caregivers. To solve this problem, we propose a novel framework for a system that teaches appropriate nursing care actions according to the current situation. The realization of such a teaching system requires technology to recognize the current situation and effectively teach the interaction between the caregiver and the patient. In this article, we propose a system that integrates depth camera-based pose estimation of the patient and Mixed Reality (MR) technology to present the target motion of the patient to a caregiver. To accurately present the patient's target pose to the novice caregivers, our system displays an avatar showing the patient's ideal animation overlaid on the actual patient. Experimental results show that our system can accurately instruct the caregiver about the patient's target pose in each movement procedure

A performance evaluation of overground gait training with a mobile body weight support system using wearable sensors

Overground gait training under body weight support (BWS) for patients who suffer from neurological injuries has been proven practical in recovering from walking ability. Conventionally, skilled therapists or additional robots are required to assist the patient’s body weight and pelvis movement, making the rehabilitation process physically and economically burdensome. We investigate if a BWS walker using only two actuators can support the user’s body weight and simultaneously protect/assist the transverse pelvis rotation, improving natural gait with minimal motion compensation. In this paper, a BWS strategy called transverse pelvis rotation support (TPRS) is proposed to enable the BWS system to generate cable tension in the forward direction, as a purpose to support transverse pelvis rotation in addition to our previously proposed static or variable BWS. Wearable sensory devices, including instrumented shoes and harness, were developed to provide real-time ground reaction force and pelvis rotation signals simultaneously. Ten non-disabled participants were unloaded with 0% ~ 15% BWS under four different controls. Vertical ground reaction force, transverse pelvis kinematics, and user experience were compared using proposed controls. One-Way repeated measures ANOVA analysis assessed if control strategies generally affect the performance. All proposed controls enable the walker to support part of the user’s body weight. SBWS-TPRS and VBWS-TPRS control enable users to achieve a significantly improved pelvic motion and prolonged single support phase than pure static BWS or variable BWS, although users perceive a higher workload under them. The proposed BWS controls show the potential to become a complementary method in gait rehabilitation

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

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 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

On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping

Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS) mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM). We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD) and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP) algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization