Multimodal Perception for Indoor Mobile Robotics Navigation and Safe Manipulation

Research toward unmanned mobile robot navigation has gained significant importance in the last decade due to its potential applications in the location-based services industry. The increase in construction of large space indoor buildings has made difficulty for humans to operate within such environments. In this study, a mobile robot's indoor navigation algorithm is developed with vision cameras. Using two monocular cameras (one looking forward and one looking downward), the developed algorithms make use of the salient features of the environments to estimate rotational and translational motions for real-time positioning of the mobile robot. At the same time, an algorithm based on artificial landmark recognition is developed. The artificial landmark is shaped arrow based signboards with different colors representing different paths. These algorithms are integrated into a designed framework for mobile robot real-time positioning and autonomous navigation. Experiments are performed to validate the designed system using the mobile robot PIONEER P3-AT. The developed algorithm was able to detect and extract artificial landmark information up to 3 m distance for the mobile robot guidance. Experiment results show an average error of 0.167 m deviation from the ideal path, signified the good ability and performance of the development autonomous navigation algorithm.

This chapter presents construction, control, advantages and disadvantages of various pneumatic actuators we have been using in several projects of climbing, walking and serpentine robots during last 13 years. We start with qualitative and quantitative analysis of different actuators. This part is mostly based on the literature review but augmented with our own experience related to these particular types of robots. We focus on pneumatic drives as very suitable for robots having permanent contact with unknown environment. Then we show a few constructions developed in our laboratory: starting from light weight manipulator for climbing robot, quadruped walker and climber, jumping and worm-like robots, see Fig. 1. We also present our contribution in development of the family of serpentine robots designed at the University of Michigan (UofM). There are some general requirements for joint actuators in mobile robots designed for regular contact with ground (obstacles). Listed here are the six most important ones: 1. Joint actuators should be capable of developing sufficient force to lift whole robot or its parts in order to climb or overcome obstacles, or to operate with load. 2. Joint angles should be controllable proportionally. 3. Another key requirement is that mobile robots should conform to the terrain compliantly. This assures correct propulsion and safe manipulation as well as dynamic isolation of main body (controller) from ground. Robots that don’t conform compliantly require complex sensor systems to measure contact forces and to command a momentary angle for each non-compliant joint accordingly. 4. At times it is necessary to increase the stiffness of a joint, for example to cross a gap or precisely track position trajectory. Alternatively, it may be necessary to adjust the stiffness to an intermediate level, for example to change jumping frequency. Thus, considered mobile robots and manipulators must be capable of adjusting the stiffness of every DOF individually and proportionally. 5. Joint actuators should be scalable to fit robots of different sizes. It is convenient to use the same technology in miniand macro-scale.

s =-. From such a property of observation, it follows that in contrast to the classical systems, the actual state of the quantum-mechanical system obtains a value, which was measured by the observer, and further evolution of the system starts from this value.In the other words, the evolution the quantum-mechanical system depends on the fact of its observation. www.intechopen.com Navigation of Quantum-Controlled Mobile Robots 313An actual evolution of the quantum-mechanical system is governed by the evolution operators, which are applied to the state matrix σ or state vector s .Below, we consider the states and operators, which are used in quantum information theory. Concepts of the quantum information theoryThe elementary state, which is considered in quantum information theory (Nielsen & Chuang, 2000), is called qubit (quantum bit) and is represented by a two-element complex vector How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following:

Path-finding is a fundamental problem, which involves for solving a planning problem seeking optimal paths from start state to a goal state. This review paper focused on usage of Radio Frequency Identification (RFID) technology in path-finding and navigation for an autonomous mobile robot. The RFID tags (IC tags) are used for the purpose of tracing the current co-ordinate/location of the Robot. Various searching algorithms are analyzed for the purpose towards navigation of mobile robot. Different soft-computing techniques like Genetic Algorithm, Neural Network, and Fuzzy Logic are considered. This research also focused on some hybrid algorithm for path-finding and navigation of mobile robot. Different methods like cellular automata, Network Simplex Method, Complete Coverage Navigation etc. are also studied.

Bayesian Meta-Learning’s role in the autonomous navigation of mobile robots remains unexplored; this systematic review highlights the effectiveness of Bayesian Meta-Learning in enhancing probabilistic mapping and navigation in mobile autonomous robots. The paper initially compares Bayesian Meta-Learning with existing techniques that have been the cornerstone of probabilistic mapping and navigation, such as Kalman filters and particle filters. It then meticulously examines the critical metrics, including path planning, obstacle avoidance, active localization, navigation in dynamic environments, and mapping accuracy, highlighting the substantial impact of Bayesian Meta-Learning in enhancing these critical aspects of autonomous navigation in mobile robots. The approach enhances adaptability and learning in mobile robots, highlighting its potential to transform autonomous navigation. In addition to emphasizing the positive outcomes of the research, the review also acknowledges a substantial research gap and aims to provide novel insights for future exploration. Future research directions include lifelong learning, uncertainty-aware exploration, integration of prior knowledge, and improvements in human-robot interaction to enhance existing paradigms and advance robust autonomous robotic systems. Therefore, this study accentuates the affirmative findings regarding Bayesian Meta-Learning and recognizes and contributes to the broader research landscape within the field.

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

The obstacle avoidance and navigation are important tasks for a mobile robot in applications such as industry, military, exploration and automated vehicles. This paper presents an implementation of mapping and navigation of autonomous mobile robot using Robot Operating System (ROS) and Orbbec Astra camera. In recent days, Lidar is used for the mapping and navigation problem by sensing the objects which are above floor level by using only a single horizontal scanning line and this may result in inaccurate generation of map leads to collision during autonomous navigation of mobile robots. In order to overcome limitation of Lidar, Orbbec Astra (Kinect) camera is used for mapping of indoor environment which can detect objects which are above and below the floor level and navigation of mobile robot from start to target location without colliding with obstacles. Experimental results show the map of the indoor environment generated using Orbbec Astra camera is matched with real indoor environment, and generated map is tested experimentally to navigate the mobile robot from start to target location without collision with obstacles.KeywordsMobile robotsMappingNavigationOrbbec Astra cameraROS

Mobile manipulation is essential for robots to accomplish everyday household chores such as set the table. In order to successfully perform manipulation tasks, a 3-D representation of the environment and the pose of the target object are needed. In this article, we propose a systematic solution for safe and efficient robot manipulation. Innovatively, a task-oriented environment modeling strategy is presented for collision-free navigation and motion planning, which integrates the grid-based 2-D map and local real-time octree-based 3-D representation. Furthermore, we introduce a task-oriented object pose estimation approach based on the fiducial marker and ontology technology. In particular, a property-driven target object inference and pose estimation algorithm is designed, which allows the mobile robot to implement the task-oriented object manipulation. The proposed solution is evaluated through real-world experiments, where memory usage, computation time, collision checking, and object pose estimation are extensively investigated. Also, application scenarios are presented to validate the effectiveness and efficiency of our proposal.

This paper presents indoor localization techniques for mobile robot and pedestrian navigation. Two different methods are examined and the performance and superiority of each method are discussed. A dead reckoning method is established for the mobile robot navigation and a wireless localization network is used for the pedestrian navigation. The purpose of the dead reckoning system is to integrate three internally-mounted sensors such as accelerometer, odometer, and heading angle sensor for positioning the mobile robot without depending on any external reference information. However, in the case of pedestrian, it is difficult to install inertia sensors to the human body. So, for the pedestrian navigation, we use a wireless sensor network. The key issue in wireless localization network is that the measurement of wireless sensor network contains lots of noises whose noise characteristics are time-varying. In this paper, we propose a simple algorithm that can reduce the noise effect without using any noise characteristics.

Intelligent mobile robot navigation, Federico Cuesta and Aníbal Ollero, Springer Tracts in Advanced Robotics, vol. 16, Springer: Berlin–Heidelberg, pages: XII + 204. Price $99.00, ISBN: 3‐540‐23956‐1 2005

One of the most concerning and costly problems in hospitals is patients falls. We address this problem by introducing PAM, a patient assistant mobile robot, that maneuvers mobility aids to assist with fall prevention. Common objects found inside hospitals include objects with legs (i.e. walkers, tables, chairs, equipment stands). For a mobile robot operating in such environments, safely maneuvering these objects without collision is essential. Since providing the robot with dynamic models of all possible legged objects that may exist in such environments is not feasible, autonomous learning of an approximate dynamic model for these objects would significantly improve manipulation planning. We describe a probabilistic method to do this by fitting pre-categorized object models learned from minimal force and motion interactions with an object. In addition, we account for multiple manipulation strategies, which requires a hybrid control system comprised of discrete grasps on legs and continuous applied forces. To do this, we use a simple one-wheel point-mass model. A hybrid MPC-based manipulation planning algorithm was developed to compensate for modeling errors. While the proposed algorithm applies to a broad range of legged objects, we only show results for the case of a 2-wheel, 4-legged walker in this paper. Simulation and experimental tests show that the obtained dynamic model is sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot can successfully move the object to a desired position without collision.

A method of fusing data from a Global Positioning System (GPS) and a Dead Reckoning (DR) system for outdoor navigation of a Wheeled Mobile Robot (WMR) is proposed. The low-cost GPS receiver cannot be utilized by itself for navigating the mobile robot. Since, it produces an error of approximately 10–20 meters. DR can provide precise navigation data to the mobile robot but its errors accumulate over time. Since, all the previous data are utilized to calculate the current position information. DR needs to be complemented by other navigation sensors to navigate the mobile robot. The proposed GPS/DR data fusion method is based on the characteristics of the single GPS receiver output. The fused data provides accurate and robust navigation information to the outdoor mobile robot. Simulations were conducted using real GPS data which were then compared with the results using a Kalman filter that verified the potential of the proposed GPS/DR data fusion method.

Reinforcement learning (RL) is suitable for navigation of a mobile robot. We overcame some difficulties of RL which are large computational cost and determination of parameter values for RL with the help of a genetic algorithm (GA) and method of parameter prediction based on results of GA and complexity measure. As a result of these proposals, we succeeded in navigating the real robot practically. In our previous studies, we just one kind of mobile robot, which has three wheels. Our RL method can decrease the computational cost for learning of navigation and development of mobile robots, provided the skill obtained by RL for one mobile robot can be transferred to other mobile robots.To verify the generalization capability of RL in navigation of a mobile robot, the present paper proposes to transfer the skill obtained by RL to a different kind of a mobile robot. We carried out the experiment and we succeeded in transferring the skill obtained by RL to a different mobile robot.

The concepts of occupancy grids and probabilistic maps were introduced at the end of the eighties. Since then, research work focussed mainly on the definition of the representation, data fusion and generation of occupancy models. Few consideration has been given to processing occupancy maps as textured images in order to extract meaningful information required for robot navigation and control of interactions with the environment. This paper investigates the application of segmentation techniques on probabilistic occupancy maps represented as textured images. Enhancements are proposed to a uniformity estimation technique based on local binary pattern and contrast (LBP/C) to achieve robust segmentation of occupancy maps that typically result from range sensors with limited resolution. The accuracy of the segmented 2D occupancy maps is demonstrated experimentally through an application on mobile robot navigation with collision avoidance.

The concepts of occupancy grids and probabilistic maps were introduced at the end of the eighties. Since then, research work focused mainly on the definition of the representation, data fusion and generation of occupancy models. Few consideration has been given to processing occupancy maps as textured images in order to extract meaningful information required for robot navigation and control of interactions with the environment. This paper investigates the application of segmentation techniques on probabilistic occupancy maps represented as textured images. Enhancements are proposed to a uniformity estimation technique based on local binary pattern and contrast (LBP/C) to achieve robust segmentation of occupancy maps that typically result from range sensors with limited resolution. The accuracy of the segmented 2D occupancy maps is demonstrated experimentally through an application on mobile robot navigation with collision avoidance