Search Results (14)

Robotic manipulators are highly valuable tools that have become widespread in the industry, as they can achieve great precision and velocity in pick and place as well as processing tasks. However, to unlock their complete potential, some problems such as inverse kinematics (IK) need to be solved: given a Cartesian target, a method is needed to find the right configuration for the robot to reach that point. Another issue that needs to be addressed when dealing with robotic manipulators is the obstacle avoidance problem. Workspaces are usually cluttered and the manipulator should be able to avoid colliding with objects that could damage it, as well as with itself. Two alternatives exist to do this: a controller can be designed that computes the best action for each moment given the manipulator’s state, or a sequence of movements can be planned to be executed by the robot. Classical approaches to all these problems, such as numeric or analytical methods, can produce precise results but take a high computation time and do not always converge. Learning-based methods have gained considerable attention in tackling the IK problem, as well as motion planning and control. These methods can reduce the computational cost and provide results for every situation avoiding singularities. This article presents a literature review of the advances made in the past five years in the use of Deep Neural Networks (DNN) for IK with regard to control and planning with and without obstacles for rigid robotic manipulators. The literature has been organized in several categories depending on the type of DNN used to solve the problem. The main contributions of each reference are reviewed and the best results are presented in summary tables. Full article

Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and this loss of detail limits the grasp performance of robots in complex environments. This paper proposes a high-performance grasp detection algorithm with a multi-target semantic segmentation model, which can effectively improve a robot’s grasp success rate in cluttered environments. The algorithm consists of two cascades: Semantic Segmentation and Grasp Detection modules (SS-GD), in which the backbone network of the semantic segmentation module is developed by using the state-of-the-art Swin Transformer structure. It can extract the detailed features of objects in cluttered environments and enable a robot to understand the position and shape of the candidate object. To construct the grasp schema SS-GD focused on important vision features, a grasp detection module is designed based on the Squeeze-and-Excitation (SE) attention mechanism, to predict the corresponding grasp configuration accurately. The grasp detection experiments were conducted on an actual UR5 robot platform to verify the robustness and generalization of the proposed SS-GD method in cluttered environments. A best grasp success rate of 91.7% was achieved for cluttered multi-target workspaces. Full article

This study focuses on addressing the problem of motion planning within workspaces cluttered with obstacles while considering temporal and input constraints. These specifications can encapsulate intricate high-level objectives involving both temporal and spatial constraints. The existing literature lacks the ability to fulfill time specifications while simultaneously managing input-saturation constraints. The proposed approach introduces a hybrid three-component control algorithm designed to learn the safe execution of a high-level specification expressed as a timed temporal logic formula across predefined regions of interest in the workspace. The first component encompasses a motion controller enabling secure navigation within the minimum allowable time interval dictated by input constraints, facilitating the abstraction of the robot’s motion as a timed transition system between regions of interest. The second component utilizes formal verification and convex optimization techniques to derive an optimal high-level timed plan over the mentioned transition system, ensuring adherence to the agent’s specification. However, the necessary navigation times and associated costs among regions are initially unknown. Consequently, the algorithm’s third component iteratively adjusts the transition system and computes new plans as the agent navigates, acquiring updated information about required time intervals and associated navigation costs. The effectiveness of the proposed scheme is demonstrated through both simulation and experimental studies. Full article

This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown workspace, such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, followed by collecting every puck to the base. Since an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used for guiding an agent to reach the puck and to bring the puck to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck carrying in 3-D cluttered unknown workspace, while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3-D cluttered bounded workspace. MATLAB simulations demonstrate the outperformance of the proposed multi-agent foraging strategy in 3-D cluttered workspace. Full article

BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps. Full article

This study addresses multi-robot distributed rendezvous controls in cluttered underwater environments with many unknown obstacles. In underwater environments, a Unmanned Underwater Vehicle (UUV) cannot localize itself, since a Global Positioning System (GPS) is not available. Assume that each UUV has multiple signal intensity sensors surrounding it. Multiple intensity sensors on a UUV can only measure the amplitude of signals generated from its neighbor UUVs. We prove that multiple UUVs with bounded speed converge to a designated rendezvous point, while maintaining the connectivity of the communication network. This study further discusses a fault detection method, which detects faulty UUVs based on local sensing measurements. In addition, the proposed rendezvous control is adaptive to communication link failure or invisible UUVs. Note that communication link failure or invisible UUVs can happen due to unknown obstacles in the workspace. As far as we know, our study is novel in developing 3D coordinate-free distributed rendezvous control, considering underwater robots that can only measure the amplitude of signals emitted from neighboring robots. The proposed rendezvous algorithms are provably complete, and the effectiveness of the proposed rendezvous algorithms is demonstrated under MATLAB simulations. Full article

In this work, we propose a hybrid control scheme to address the navigation problem for a team of disk-shaped robotic platforms operating within an obstacle-cluttered planar workspace. Given an initial and a desired configuration of the system, we devise a hierarchical cell decomposition methodology which is able to determine which regions of the configuration space need to be further subdivided at each iteration, thus avoiding redundant cell expansions. Furthermore, given a sequence of free configuration space cells with an arbitrary connectedness and shape, we employ harmonic transformations and harmonic potential fields to accomplish safe transitions between adjacent cells, thus ensuring almost-global convergence to the desired configuration. Finally, we present the comparative simulation results that demonstrate the efficacy of the proposed control scheme and its superiority in terms of complexity while yielding a satisfactory performance without incorporating optimization in the selection of the paths. Full article

In this work, we consider the autonomous object transportation problem employing a team of mobile manipulators within a compact planar workspace with obstacles. As the object is allowed to translate and rotate and each robot is equipped with a manipulator consisting of one or more moving links, the overall system (object and mobile manipulators) should adapt its shape in a flexible way so that it fulfills the transportation task with safety. To this end, we built a sequence of configuration space cells, each of which defines an allowable set of configurations of the object, as well as explicit intervals for each manipulator’s states. Furthermore, appropriately designed under- and over-approximations of the free configuration space are used in an innovative way to guide the configuration space’s exploration without loss of completeness. In addition, we coupled methodologies based on Reference Governors and Prescribed Performance Control with harmonic maps, in order to design a distributed control law for implementing the transitions specified by the high-level planner, which possesses guaranteed invariance and global convergence properties, thus avoiding the requirement for synchronized motion as inherently dictated by the majority of the related works. Furthermore, the proposed low-level control law does not require continuous information exchange between the robots, which rely only on measurements of the object’s configuration and their own states. Finally, a transportation scenario within a complex warehouse workspace demonstrates the proposed approach and verifies its efficiency. Full article

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively. Full article

In this paper, we present a mixed-initiative motion control strategy for multiple quadrotor aerial vehicles. The proposed approach incorporates formation specifications and motion-planning commands as well as inputs by a human operator. More specifically, we consider a leader–follower aerial robotic system, which autonomously attains a specific geometrical formation, by regulating the distances among neighboring agents while avoiding inter-robot collisions. The desired formation is realized by a decentralized prescribed performance control strategy, resulting in a low computational complexity implementation with guaranteed robustness and accurate formation establishment. The multi-robot system is safely guided towards goal configurations, by employing a properly defined navigation function that provides appropriate motion commands to the leading vehicle, which is the only one that has knowledge of the workspace and the goal configurations. Additionally, the overall framework incorporates human commands that dictate the motion of the leader via a teleoperation interface. The resulting mixed-initiative control system has analytically guaranteed stability and convergence properties. A realistic simulation study, considering a team of five quadrotors operating in a cluttered environment, was carried out to demonstrate the performance of the proposed strategy. Full article

Multirobot cooperation enhancing the efficiency of numerous applications such as maintenance, rescue, inspection in cluttered unknown environments is the interesting topic recently. However, designing a formation strategy for multiple robots which enables the agents to follow the predefined master robot during navigation actions without a prebuilt map is challenging due to the uncertainties of self-localization and motion control. In this paper, we present a multirobot system to form the symmetrical patterns effectively within the unknown environment deployed randomly. To enable self-localization during group formatting, we propose the sensor fusion system leveraging sensor fusion from the ultrawideband-based positioning system, Inertial Measurement Unit orientation system, and wheel encoder to estimate robot locations precisely. Moreover, we propose a global path planning algorithm considering the kinematic of the robot’s action inside the workspace as a metric space. Experiments are conducted on a set of robots called Falcon with a conventional four-wheel skid steering schematic as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces exact robot locations after sensor fusion with the feasible formation tracking of multiple robots system on the simulated and real-world experiments. Full article

In this work, we consider the motion control problem for a platoon of unicycle robots operating within an obstacle-cluttered workspace. Each robot is equipped with a proximity sensor that allows it to perceive nearby obstacles as well as a camera to obtain its relative position with respect to its preceding robot. Additionally, no robot other than the leader of the team is able to localize itself within the workspace and no centralized communication network exists, i.e., explicit information exchange between the agents is unavailable. To tackle this problem, we adopt a leader–follower architecture and propose a novel, decentralized control law for each robot-follower, based on the Prescribed Performance Control method, which guarantees collision-free tracking and visual connectivity maintenance by ensuring that each follower maintains its predecessor within its camera field of view while keeping static obstacles out of the line of sight for all time. Finally, we verify the efficacy of the proposed control scheme through extensive simulations. Full article

This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no sensing hole remains. As far as we know, this article is novel in enabling multiple heterogeneous robots to build underwater sensor networks in a 3D cluttered environment, while satisfying the following conditions: (1) Remove all sensing holes. (2) Network connectivity is maintained. (3) Localize all underwater robots. In addition, we address how to handle the case where a robot is broken, and we discuss how to estimate the number of robots required, considering the case where an obstacle inside the workspace is not known a priori. Utilizing MATLAB simulations, we demonstrate the effectiveness of the proposed network construction methods. Full article

Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length. Full article