Search

Transport of cargo by short-sea shipping is at the forefront of the European Union's transportation policy. It has the promise to alleviate congested land transport and decrease pollution [European Commission. 2021. Putting European transport on track for the future]. However, the shortage of qualified personnel might impede this solution. Autonomous sailing of ships could help, especially if the whole trip is automated. In this study a control system is designed that is capable of sailing a round-trip with a model-scale feeder vessel in Marin's Seakeeping and Manoeuvring Basin. The Guidance-Navigation-Control framework is used to sail this journey with attention given to smooth switching between the operational modes. A set of experiments is conducted to validate the controller and identify crucial parts in the operation. The experiments showed that the controlled vessel can sail a round-trip with different levels of waves and wind disturbances coming from different directions. Furthermore, the round-trip can be modified such that the approach angle to the quay changes and the ship still successfully docks at the quay. It was found that the approach to the quay is most difficult during transition from high to low speed, when the bow thrusters have yet to become effective, and a large change in course is needed. This can be circumvented by changing the trajectory to the quay to have a longer straight approach, or it can be corrected when the bow thrusters become active. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aromatic hydrocarbons generally refer to compounds containing benzene rings. Many types of isomers can be formed by replacing hydrogen atoms on the benzene ring. In this paper, an aromatic-hydrocarbon-inspired modular robot (AHIMR) is proposed. The robot can be reassembled into different configurations suitable for various task requirements. A vision-based docking system is designed for the AHIMR. The system primarily consists of two stages: a remote guidance stage and a precise docking stage. During the remote guidance stage, an object module is identified using an illumination adaptive target recognition algorithm, and then the active module moves to the docking area through communication with ZigBee. In the precise docking stage, the active module calculates the relative pose with the object module using a perspective-n-point method and dynamically adjusts its posture to dock. In this process, a Kalman filter is used to reduce target occlusion and jitter interference. In addition, the docking system feasibility is verified via several simulation experiments. The module docking accuracy is controlled within 0.01 m, which meets the reconfiguration task requirements of the AHIMR. In the AHIMR submodule docking experiment, the active module accurately moves to the expected position with a docking success rate of 95%. [ABSTRACT FROM AUTHOR]

This paper deals with maneuver control for the autonomous docking of two small spacecraft in a rendezvous flight. Due to hardware constraints, maneuver control suppressing fuel consumption and computational cost is a significant issue for small spacecraft. Here, the maneuver control for the approaching motion used a model predictive control system. The spacecraft's maneuvers in two-dimensional plane motion were performed in a frictionless environment with air bearings to verify the control performance.

In this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the A* ${A}^{* }$ algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to "grasp" the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers. [ABSTRACT FROM AUTHOR]

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process. [ABSTRACT FROM AUTHOR]

Docking is a safety-critical operation for autonomous surface vehicles and requires highly accurate navigation signals. Since Global Navigation Satellite Systems (GNSS) can be unreliable and inaccurate in urban environments, other sensors should be considered for increased redundancy and reliability. To this end, we present a low-cost visual-inertial navigation system that we can use for automatic docking of small vehicles. The proposed system produces state estimates of the vehicle, including position, velocity, and attitude, based on raw image and inertial data. To simplify the navigation task, we use easily identifiable tags as a reference on the dockside. When the vehicle approaches the dock, a visual fiducial system recognizes the tags and estimates the relative pose between the camera onboard the vehicle and the tags at the dockside. The camera-tag pose and inertial data are then fused using an error-state Kalman filter for robust state estimation of the vehicle. For benchmarking, we use an unmanned surface vehicle equipped with a dual-antenna real-time kinematic GNSS receiver for accurate positioning and heading. We show that the proposed method performs well on regular and adverse weather data. Finally, we demonstrate that the proposed method performs well in feedback control through field experiments and can supplement traditional navigation systems for docking operations.

Abstract Positioning and navigation technology is a new trend of research in mobile robot area. Existing researches focus on the indoor industrial problems, while many application fields are in the outdoor environment, which put forward higher requirements for sensor selection and navigation scheme. In this paper, a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented. The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios. The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances. Based on the Bezier curve, a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path. The Kalman filter is established to process the guidance signals and control outputs of the motion controller. Finally, the autonomous positioning and docking experiment are carried out. The results of the research verify the effectiveness of the hybrid navigation, which can be used in autonomous warehousing logistics and multi-mobile robot system.

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.

Autonomous recharging is a fundamental requirement for autonomous mobile robots in order to allow them to work continuously without human intervention, and autonomous docking to a charging station is a challenging yet promising research area. In particular, the docking task becomes even more complicated in unstructured human environments when the charging station’s location is not fixed and when there are dynamic obstacles moving around. This paper presents a laser intensity based autonomous docking approach, which allows a mobile robot to recharge its battery autonomously in unstructured environments. Laser reflection intensity for a self-adhesive reflective tape is quantitatively investigated, and the sufficient/necessary conditions for successful reflector detection are discussed. Using the proposed reflector detection technique, the charging station can be easily distinguished from similar objects in an unstructured environment. Comparing to the traditional docking methods, the developed approach is easier to implement and can significantly improve the reliability of autonomous docking and recharging. Extensive experiments were conducted to verify the effectiveness of the developed autonomous docking approach.

Positioning and navigation technology is a new trend of research in mobile robot area. Existing researches focus on the indoor industrial problems, while many application fields are in the outdoor environment, which put forward higher requirements for sensor selection and navigation scheme. In this paper, a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented. The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios. The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances. Based on the Bezier curve, a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path. The Kalman filter is established to process the guidance signals and control outputs of the motion controller. Finally, the autonomous positioning and docking experiment are carried out. The results of the research verify the effectiveness of the hybrid navigation, which can be used in autonomous warehousing logistics and multi-mobile robot system. [ABSTRACT FROM AUTHOR]

Accurate maneuvering estimation is essential to establish autonomous berthing control. The system-based mathematical model is widely used to estimate the ship's maneuver. Commonly, the system parameters of the mathematical model are obtained by the captive model test (CMT), which is time-consuming to construct an accurate model suitable for complex berthing maneuvers. System identification (SI) is an alternative to constructing the mathematical model. However, SI on the mathematical model of ship's maneuver has been only conducted on much simpler maneuver: turning and zig-zag. Therefore, this study investigates the SI on a mathematical model capable of berthing maneuver. The main contributions of this study are as follows: (i) construct the system-based mathematical model on berthing by optimizing system parameters with a reduced amount of model tests than the CMT-based scheme; (ii) Find the favorable choice of objective function and type of training data for optimization. Global optimization scheme CMA-ES explored the system parameters of the MMG model from the free-running model's trajectories. The berthing simulation with the parameters obtained by the proposed method showed better agreement with the free-running model test than parameters obtained by the CMT. Furthermore, the proposed method required fewer data amounts than a CMT-based scheme. [ABSTRACT FROM AUTHOR]

Autonomous Surface Vehicles (ASVs) are bound to play a fundamental role in the maintenance of offshore wind farms. Robust navigation for inspection vehicles should take into account the operation of docking within a harbouring structure, which is a critical and still unexplored maneuver. This work proposes an end-to-end docking approach for ASVs, based on Reinforcement Learning (RL), which teaches an agent to tackle collision-free navigation towards a target pose that allows the berthing of the vessel. The developed research presents a methodology that introduces the concept of illegal actions to facilitate the vessel's exploration during the learning process. This method improves the adopted Actor-Critic (AC) framework by accelerating the agent's optimization by approximately 38.02%. A set of comprehensive experiments demonstrate the accuracy and robustness of the presented method in scenarios with simulated environmental constraints (Beaufort Scale and Douglas Sea Scale), and a diversity of docking structures. Validation with two different real ASVs in both controlled and real environments demonstrates the ability of this method to enable safe docking maneuvers without prior knowledge of the scenario. • The novel concept of illegal actions to accelerate the optimization of an RL agent. • Situational awareness computed without prior knowledge of the scenario. • Autonomous docking of surface vehicles through an RL framework. • Extensive experiments in both simulated and real scenarios with two different vessels. [ABSTRACT FROM AUTHOR]

Modular robots have the advantage of self-assembling into a large and complex structure to travel through territories beyond an individual robot's capacity. A swarm of mobile robots is combined through mechanical interconnection and joint actuation to achieve a linked or articular configuration. In this paper, to enhance the perception, actuation and docking capacity of modular robots, a parallel mechanism-based docking system and onboard visual perception system are proposed in the design of a novel compact self-assembling mobile modular robot (SMMRob). Each module is self-contained, with a sensing or joint function. The robot modules can dock with each other based on relative positioning, which employs the visual perception of passive markers or active infrared signals in different localizations. Performance experiments were conducted to evaluate the robot module. Docking experiments were performed, along with an analysis of the success and failure results. The self-assembly of snake-like and quadruped robots was achieved in response to different environments, including an obstacle, gap or stair, and experiments were performed on self-assembly into a snake-like structure. [ABSTRACT FROM AUTHOR]

This paper presents autonomous docking of an inhouse built resident Remotely Operated Vehicle (ROV), called Rover ROV, through acoustic guided techniques. A novel cage-type docking station has been developed. The docking station can be placed on a deep-sea lander, taking the Rover ROV to the seafloor. Instead of using vision-based pose estimation techniques and expensive navigation sensors, the Rover ROV docking adopts an ultra-short baseline (USBL) and low-cost inertial sensors to build an adaptive fault-tolerant integrated navigation system. To solve the problem of sonar-based failure positioning, the measurement residuals are exploited to detect measurement faults. Then, an adaptation scheme for estimating the statistical characteristics of noise in real-time is proposed, which can provide robust and smooth positioning results. It is more suitable for a compact and low-cost deep-sea resident ROV. Field experiments have been conducted successfully in the Qiandao Lake and the South China Sea area with a depth of 3000 m, respectively. The experimental results show that the functionality of autonomous docking has been achieved. Under the guidance of the navigation system, the Rover ROV can autonomously and efficiently return to the docking station within a range of 100 m even when the amounts of outliers exist in the acoustic positioning data. These achievements can be applied to current ROVs by an easy retrofit. [ABSTRACT FROM AUTHOR]

This article proposes an autonomous docking control for nonholonomic constrained mobile robots and applies it to an intelligent mobility device or wheelchair for assisting the user in approaching resting furniture such as a chair or a bed. We defined a virtual landmark inferred from the target docking destination. Then, we solve the problem of keeping the targeted volume inside the field of view (FOV) of a tracking camera and docking to the virtual landmark through a novel definition that enables to control for the desired end pose. In this article, we proposed a nonlinear feedback controller to perform the docking with the depth camera's FOV as a constraint.Then, a numerical method is proposed to find the feasible space of initial states where convergence could be guaranteed. Finally, the entire system was embedded for real-time operation on a standing wheelchair with the virtual landmark estimation by three-dimensional object tracking with an RGB-D camera and we validated the effectiveness in simulation and experimental evaluations. The results show the guaranteed convergence for the feasible space depending on the virtual landmark location. In the implementation, the robot converges to the virtual landmark while respecting the FOV constraints. [ABSTRACT FROM AUTHOR]

As wave energy conversion technology advances, recharge of autonomous underwater vehicles has emerged as a promising application for this at-sea power. We bring together an interdisciplinary team to create a simulation framework linking hydrodynamic modeling, autonomous docking and navigation algorithms, and a power tracking model to better understand how a full wave energy converter–autonomous underwater vehicle system could be modeled. A floating point absorber wave energy converter is modeled and analyzed under various wave conditions. We incorporate three different dock designs, using the modeled dock motion and simulated wave-induced currents to test our autonomous docking algorithm. We couple the output of this algorithm to the hydrodynamic model to simulate autonomous docking. This shows that docking with a floating third body is successful in most sea states, while a dock rigidly mounted to the wave energy converter presents difficulty for autonomous docking. Finally, we incorporate a power model to better understand the feasibility and capabilities of a wave energy converter–underwater vehicle system in simulated wave environments. This shows that this system is comfortably supported in the majority of sea states, and provides an estimate of the on-board power storage required to maximize vehicle mission time. • Wave energy converter and autonomous vehicle dock modeled using conditions found at Oregon Pacific Ocean test site. • Autonomous underwater vehicle docking algorithm demonstrated for three different modeled docks under various wave conditions. • Power model demonstrates vehicle mission capabilities with modeled wave energy converter power output. [ABSTRACT FROM AUTHOR]

The present study aimed to propose translational and rotational control of a chaser spacecraft in the close vicinity docking phase with a target subjected to external disturbances. For this purpose, two sliding mode controls (SMC) are developed to coordinate the relative position and attitude of two spacecraft. The chaser is guided to the tumbling target by the relative position control, approaching in the direction of the target docking port. At the same moment, the relative attitude control coordinates the chaser attitude so that it can be aligned with the target orientation. These control systems regulate the relative translational and rotational velocities to be zero when two spacecraft are docking. The robustness of the closed-loop system in the presence of external disturbances, measurement noises and uncertainties is guaranteed by analyzing and calculating the control gains via the Lyapunov function. The simulations in different scenarios indicated the effectiveness of the controller scheme and precise maneuver regarding the accuracy of docking conditions. [ABSTRACT FROM AUTHOR]

Modular robots have the advantage of self-assembling into a large and complex structure to travel through territories beyond an individual robot’s capacity. A swarm of mobile robots is combined through mechanical interconnection and joint actuation to achieve a linked or articular configuration. In this paper, to enhance the perception, actuation and docking capacity of modular robots, a parallel mechanism-based docking system and onboard visual perception system are proposed in the design of a novel compact self-assembling mobile modular robot (SMMRob). Each module is self-contained, with a sensing or joint function. The robot modules can dock with each other based on relative positioning, which employs the visual perception of passive markers or active infrared signals in different localizations. Performance experiments were conducted to evaluate the robot module. Docking experiments were performed, along with an analysis of the success and failure results. The self-assembly of snake-like and quadruped robots was achieved in response to different environments, including an obstacle, gap or stair, and experiments were performed on self-assembly into a snake-like structure.

This paper deals with the case of a target satellite in an unknown orientation and location with respect to the master satellite. Feature based monocular pose estimation vision system was presented. The results of analysis, implementation and testing of simulation intended for vision-based navigation applications such as rendezvous of satellites and formation flying are shown. The mobile robot was used as the platform for the vision system. Pose estimation algorithms were implemented in Matlab environment. It was obtained that the proposed method is robust on varying and low light conditions.

Multiple mobile robots in formation are often required to dock to each other to overcome the limitations, such as battery failure, transportation capacity, and maneuverability on rough terrains; however, it is challenging to design a single controller that navigates the robots to dock to each other, maintains the other robots in formation, and is applicable to both docked and nondocked robots, while it is also robust to uncertainties and disturbances. This article proposes a novel robust subsumption architecture for nonholonomic mobile robots in formation with docking capability. In addition to docking, the robots, i.e., all the nondocked robots and the front-docked robots, maintain a formation that can also be switched automatically to other configurations when necessary and avoid collisions with other robots and dynamic obstacles. The proposed subsumption control architecture takes into account each follower’s desired goal as well as its docking condition to synthesize a control law as a velocity control signal that is then used to determine the robust input torque for each follower using the robots’ dynamics. The Lyapunov stability of the controller is also proved. We also develop strategies for efficient centralized motion planning of the followers to achieve various goals, e.g., formation keeping/switching, docking, and collision avoidance. The effectiveness of our proposed methodology was verified in simulations as well as implementations on a virtual robot environment. Note to Practitioners—Multiple mobile robots, especially when operating as a formation, are able to perform tasks that are beyond the capabilities of individual robots. Existing formation control approaches neglect some realistic limitations of mobile robots, such as battery failure, limited transportation capacity, and maneuverability, to name a few. This article was motivated by these realistic limitations of mobile robots when operating in formation, and it suggests a new approach for navigation of such robots by docking some (or all) of these robots to each other and pursue a variety of goals. The goal includes autonomous docking, formation keeping/switching, and collision avoidance in dynamic environments. We include robot dynamics and system uncertainties in our algorithm and provide a robust control methodology. Therefore, the developed methodologies in this article can be adopted in real applications that require robots to be supplied with sufficient battery or having a large payload capacity, e.g., agricultural robotics. [ABSTRACT FROM AUTHOR]

This paper investigates the coupled position and attitude control problem of an on-orbit servicing spacecraft autonomous docking to a three-axis freely tumbling target in space. A compound control law is presented to guarantee that the docking port of servicing spacecraft is always directing towards the docking port of tumbling target, which is accomplished through the combination of the coupled relative position tracking and relative attitude control. For the purpose of avoiding collision between the two spacecraft, a two-phased approach for the terminal approaching the tumbling target is proposed. Also, the compound control is composed of a nonlinear feedback control law and an integral sliding mode control law. The nonlinear feedback control law is mainly used to track the system command and the integral sliding mode control law is mainly used to deal with the external disturbances and system uncertainties to enhance the robustness of the control system. Furthermore, the control saturation problem is considered. In addition, the characteristic of integral sliding mode under the control constraint and measurement noise is also analyzed. Finally, several numerical simulations are performed to verify the effectiveness and robustness of the compound control law for autonomous docking to a three-axis freely tumbling target. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous docking system with novel integrated algorithms for mobile self-reconfigurable robots equipped with inexpensive sensors. A novel docking algorithm was developed to determine the initial distance and orientation of the two modules, and sensor models were established through experiments. Both Extended Kalman filter (EKF) and particle filter (PF) were deployed to fuse the measurements from IR and encoders and provide accurate estimates of orientation and distance. Simulation experiments were carried out first and then real experiments were conducted to verify the feasibility and good performance of the proposed docking algorithm and system. The proposed system provides a robust and reliable docking solution using low cost sensors.

Featured Application: This manuscript developed a new self-assembly modular robot (SMR) system SambotII and provided a new vision-based method for efficient and accurate autonomous docking of SMRs. The present work lays a foundation for the future research of modular and swarm robots. Based on the present hardware and software platforms, complex behaviors and various tasks can be achieved on SambotII in the future, such as environment exploration, path planning, robotic swarm control, morphology control and, etc. A new self-assembly modular robot (SMR) SambotII is developed based on SambotI, which is a previously-built hybird type SMR that is capable of autonomous movement and self-assembly. As is known, SambotI only has limited abilities of environmental perception and target recognition, because its STM-32 processor cannot handle heavy work, like image processing and path planning. To improve the computing ability, an x86 dual-core CPU is applied and a hierarchical software architecture with five layers is designed. In addition, to enhance its perception abilities, a laser-camera unit and a LED-camera unit are employed to obtain the distance and angle information, respectively, and the color-changeable LED lights are used to identify different passive docking surfaces during the docking process. Finally, the performances of SambotII are verified by docking experiments. [ABSTRACT FROM AUTHOR]

Docking trajectory planning for unmanned surface vehicles is challenging due to complicated obstacle-avoidance in the harbor and highly nonlinear motion equations. In this paper, a two-stage (i.e., searching and optimization) hierarchical method is developed for a safe and efficient solution. In the searching stage, we establish a grid map for the harbor environment. The unmanned surface vehicle is represented by multiple featured discs and an artificially-guided Hybrid A* algorithm is implemented to search a coarse collision-free path. In the optimization stage, we first estimate the total sailing time under time-optimal maneuvering assumptions. Given the discretization scheme, we resample state and control variables along the coarse path by interpolation. Then, we construct a series of safe sailing corridors along the interpolated results. The within-corridor constraints are used to reformulate the optimal control problem, and the interpolated results are used for warm-start. The developed method is numerically validated by two docking scenarios, where high-quality trajectory as well as profiles of control inputs can be obtained with satisfactory computational efficiency. Meanwhile, it shows superiority in robustness over traditional planning techniques in the presence of narrow waterways and concave harbor shores. An animation of the simulation results is available at www.bilibili.com/video/BV14D4y1t7QL/. • A hierarchical autonomous docking trajectory planning method for USV is proposed. • AGHA* avoids pointless searching around concave harbor shore. • Within-SSC formulation is independent from number and shape of obstacles around. • Constraints can be strictly satisfied due to the benefit of optimization phase. • Comparisons highlight computational efficiency and robustness of the method. [ABSTRACT FROM AUTHOR]

A new self-assembly modular robot (SMR) SambotII is developed based on SambotI, which is a previously-built hybird type SMR that is capable of autonomous movement and self-assembly. As is known, SambotI only has limited abilities of environmental perception and target recognition, because its STM-32 processor cannot handle heavy work, like image processing and path planning. To improve the computing ability, an x86 dual-core CPU is applied and a hierarchical software architecture with five layers is designed. In addition, to enhance its perception abilities, a laser-camera unit and a LED-camera unit are employed to obtain the distance and angle information, respectively, and the color-changeable LED lights are used to identify different passive docking surfaces during the docking process. Finally, the performances of SambotII are verified by docking experiments.

Visual based navigation systems are considered essential tools in the framework of close proximity space operations, such as rendezvous and docking, both in the role of primary devices and in the role of back-up systems. Autonomy in such operations is a requirement that has been increasingly underlined, especially in the case of non-cooperative tumbling targets, as for example in the active debris removal concepts. In such a case the time delays and the partial communication coverage make the human intervention unsuitable. On the other hand, robustness of the guidance and control system is certainly an issue for autonomous docking missions. In this paper, algorithms for autonomous relative navigation by means of a single camera are detailed, and tested both numerically and experimentally. At the same time a guidance strategy has been defined in order to increase the system robustness. In order to prove the soundness of the proposed navigation and guidance approach a docking mission has been successfully performed by means of two free floating platforms – a chaser and a target – on an air-bearing table, both in a nominal and in a non-nominal (i.e. with a tumbling target) scenarios. [ABSTRACT FROM AUTHOR]

Autonomous robots (manipulators or vehicles) may accumulate significant errors during their long-range motion to a desired position and orientation (pose). These errors, however, can be compensated for by subsequent local, short-range corrective actions to within random noise levels of the system. This paper presents a generic localization method for high-precision parallel kinematic mechanisms (PKMs) in order to allow them to accurately achieve their desired poses. The proposed method employs a novel non-contact spatial sensing technique combined with an iterative pose-correction procedure. The proposed sensing technique is based on the use of multiple spatial lines-of-sights (LOSs) emanating from a single source and 'hitting' a planar position sensitive detector (PSD) placed on the PKM's platform. Using the positional feedback provided by the PSD, the instantaneous actual pose of the platform is accurately estimated. A pose-correction method is subsequently invoked to iteratively guide the platform to its desired location within noise levels. Extensive simulations were carried out to illustrate the effectiveness of the proposed localization method for a spatial PKM being developed in our laboratory. [ABSTRACT FROM AUTHOR]

This article presents a novel docking system developed for miniature underwater robots. Recent years have seen an increased diffusion of robots for ocean monitoring, exploration and maintenance of underwater infrastructures. The versatility of those vehicles is extremely affected and limited by energetic constraints and difficulties in updating their mission parameters. Submerged docking stations are a promising solution for providing energy sources and data exchange, thus extending autonomy and mission duration of underwater robots. Furthermore, the docking capability is a novel, but promising approach to enable modularity and reconfigurability in underwater robotics. The authors here propose a hybrid docking system composed of a magnetic alignment unit and a mechanical connection. The former passively aligns and guides the underwater vehicle facilitating a subsequent mechanical connection. The reliability of the system is both analytically investigated and experimentally validated. Finally, the mechanical design of the docking system of two miniature underwater robots is described in detail. [ABSTRACT FROM AUTHOR]

The most important feature of a Self-Reconfigurable Robot (SRR) is that it is reconfigurable and self-repairing. At the centre of these capabilities is autonomous docking. One difficulty for docking is the alignment between two robots. Current strategies overcome this by integrating a mechanical guiding device within the connecting mechanism. This increases the robustness of docking but compromises the flexibility of reconfiguration. In this paper, we present a new autonomous docking strategy that can overcome the drawbacks of current approaches. The new strategy uses a novel hook-type connecting mechanism and multi-sensory guidance. The hook-type connecting mechanism is strong and rigid for reliable physical connection between the modules. The multisensory docking strategy, which includes visual-sensorguided rough positioning, Hall-sensor-guided fine positioning, and the locking between moving and target modules, guarantees robust docking without sacrificing reconfigurability. The proposed strategy is verified by docking between a worm-shaped robot and one target module, and docking among three moving robots to form a T-shaped configuration. The experimental results showed that the strategy is very effective. [ABSTRACT FROM AUTHOR]

Abstract: This study presents adaptive tracking controls of relative position between two spacecraft in the presence of uncertainties in the thrust alignments and gains, and the active spacecraft's mass. The proposed methods are based on the equations of motion expressed in the general second-order form with trajectory-controllable Hamiltonian systems, which makes it possible to use some useful physical properties in the control law design. Unlike most of the previous works, the control laws are presented not only for the Cartesian coordinates, but also for the spherical coordinates in Keplerian orbits whose dynamic equation is highly complicated but reflects the actual measurement environment (laser/microwave based) and thus is more suitable in real applications. The proposed adaptive algorithms are developed utilizing the smooth-projection algorithm in order to deal with the uncertainties in the actuator model. Numerical simulations show that the proposed adaptive scheme successfully achieves the relative position tracking within a small level of error in the whole process, compared with the non-adaptive scheme. [Copyright &y& Elsevier]

Abstract: Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space, common in reinforcement learning, makes it difficult to be used in real-world scenarios. In this work we describe a novel real-world reinforcement learning method. It uses a supervised reinforcement learning approach combined with Gaussian distributed state activation. We successfully tested this method in two real scenarios of humanoid robot navigation: first, backward movements for docking at a charging station and second, forward movements to prepare grasping. Our approach reduces the required learning steps by more than an order of magnitude, and it is robust and easy to be integrated into conventional RL techniques. [Copyright &y& Elsevier]

Inspired by the swarm behaviours of social insects, research into the self-assembly of swarm robots has become an attractive issue in the robotic community. Unfortunately, there are very few platforms for self-assembly and locomotion in the field of swarm robotics. The Sambot is a novel self-assembling modular robot that shares characteristics with swarm robots and self-reconfigurable robots. Each Sambot can move autonomously and connect with the other. This paper discusses the concept of combining self-assembly and locomotion for swarm robots. Distributed control algorithms for self-assembly and locomotion are proposed. Using five physical Sambots, experiments were carried out on autonomous docking, self-assembly and locomotion. Our control algorithm for self-assembly can also be used to realize the autonomous construction and self-repair of robotic structures consisting of a large number of Sambots. [ABSTRACT FROM PUBLISHER]

The design and structure of a self-assembly modular robot (Sambot) are presented in this paper. Each module has its own autonomous mobility and can connect with other modules to form robotic structures with different manipulation abilities. Sambot has a versatile, robust, and flexible structure. The computing platform provided for each module is distributed and consists of a number of interlinked microcontrollers. The interaction and connectivity between different modules is achieved through infrared sensors and Zigbee wireless communication in discrete state and control area network bus communication in robotic configuration state. A new mechanical design is put forth to realize the autonomous motion and docking of Sambots. It is a challenge to integrate actuators, sensors, microprocessors, power units, and communication elements into a highly compact and flexible module with the overall size of 80 mm × 80 mm × 102 mm. The work describes represents a mature development in the area of self-assembly distributed robotics. [ABSTRACT FROM PUBLISHER]