10 research outputs found
Autonomous cooperative visual navigation for planetary exploration robots
No full textPlanetary robotics navigation has attracted the great attention of many researchers in recent years. Localization is one of the most important problems for robots on another planet in the lack of GPS. The robots need to be able to know their location and the surrounding map in the environment concurrently, to work and communicate together on another planet. In the current work, a novel algorithm is designed to cooperatively localize a team of robots on another planet. Consequently, a robust algorithm is developed for cooperative Visual Odometry (VO) to localize each robot in a planetary environment while detecting both intra-loop closure and inter-loop closures using previously observed area by the robot and shared area from other robots, respectively. To validate the proposed algorithm, a comparison is provided between the proposed cooperative VO and the single version of VO. Accordingly, a planetary analogue real dataset is employed to investigate the accuracy of the proposed algorithm. The results promise the concept of cooperative VO to significantly increase the accuracy of localization
SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
Get PDFThe important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications
Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties
Get PDFThe control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties
Distributed Cooperative Visual Odometry For Planetary Exploration Rovers
No full textNavigation is a basic skill for planetary exploration robots. In the last years, planetary robotics navigation has become an important research field that includes all the robot capabilities such as perception, localisation, and mapping. This paper provides a novel algorithm to cooperatively localise a distributed team of robots on another planet when the initial pose of the robots is unknown. To perform this, a single Visual Odometry (VO) algorithm based on the conventional KLT feature tracker is employed to localise each single exploration rover in a planetary environment while detecting loop closures. The trajectory of the robots is described by a pose graph form in the designed Cooperative VO (CVO) algorithm. Detecting loop closure from the previously observed areas by the robots leads to triggering the optimisation process and improving the accuracy of the localisation. Accordingly, a planetary analogue real dataset is used to investigate the accuracy of the proposed algorithm. The superiority of the proposed distributed CVO is proved by comparing the obtained results with the single VO algorithm
Distributed Cooperative Visual Odometry For Planetary Exploration Rovers
No full textNavigation is a basic skill for planetary exploration robots. In the last years, planetary robotics navigation has become an important research field that includes all the robot capabilities such as perception, localisation, and mapping. This paper provides a novel algorithm to cooperatively localise a distributed team of robots on another planet when the initial pose of the robots is unknown. To perform this, a single Visual Odometry (VO) algorithm based on the conventional KLT feature tracker is employed to localise each single exploration rover in a planetary environment while detecting loop closures. The trajectory of the robots is described by a pose graph form in the designed Cooperative VO (CVO) algorithm. Detecting loop closure from the previously observed areas by the robots leads to triggering the optimisation process and improving the accuracy of the localisation. Accordingly, a planetary analogue real dataset is used to investigate the accuracy of the proposed algorithm. The superiority of the proposed distributed CVO is proved by comparing the obtained results with the single VO algorithm
SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
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On the Design of Human-Robot Collaboration Gestures
Get PDFEffective communication between humans and collaborative robots is essential for seamless Human-Robot Collaboration (HRC). In noisy industrial settings, nonverbal communication, such as gestures, plays a key role in conveying commands and information to robots efficiently. While existing literature has thoroughly examined gesture recognition and robots' responses to these gestures, there is a notable gap in exploring the design of these gestures. The criteria for creating efficient HRC gestures are scattered across numerous studies. This paper surveys the design principles of HRC gestures, as contained in the literature, aiming to consolidate a set of criteria for HRC gesture design. It also examines the methods used for designing and evaluating HRC gestures to highlight research gaps and present directions for future research in this area
Exploring tasks and challenges in human-robot collaborative systems:A review
Get PDFThis paper presents an in-depth exploration of Human-Robot Collaborative Systems (HRCSs) within industrial environments, a dynamic field that has witnessed significant advancements due to technological innovation and the increasing integration of Artificial Intelligence (AI). As industries evolve towards more collaborative and adaptive manufacturing systems, the dynamic interaction between humans and robots becomes pivotal. This study reviews the current state of HRCSs, focusing on the challenges of task allocation and skill alignment, safety, trust, and the psychological wellbeing of human workers. We review control strategies and architectural frameworks that underpin effective human-robot interactions (HRI), emphasising the critical role of AI in enhancing decision-making processes and the adaptability of collaborative efforts. Our review sheds light on the complexities involved in designing HRCSs that are not only efficient but also cognisant of the human experience, advocating for a balanced approach that leverages the strengths of both human and robotic counterparts. We argue that research in and implications of HRCSs should extend beyond technical considerations, touching on ethical, social, and organisational dimensions, thereby contributing to the broader discourse on the future of work in the era of Industry 4.0 and future Industry 5.0
Technical and System Requirements for Industrial Robot-as-a-Service (IRaaS)
No full textIndustrial Robot as a Service (IRaaS) is a new business model that enables SMEs to quickly adapt to changing production demands. The IRaaS model introduces industrial robots that can be rented and used on demand, requiring little to no programming knowledge. With this shift from capital expenditures (CAPEX) to operational expenditures (OPEX), the cost of ownership and expertise barriers are eliminated, allowing SMEs to avoid large upfront costs and providing greater flexibility. In this paper, the requirements for developing and realizing these new IRaaS services are identified. The functional requirements are identified by first eliciting the non-functional requirements and proposing robotic concepts that can be servitized. The requirements are also mapped into IRaaS concepts and converged to the underlying challenges
