This paper proposes a mobile robot recovery mechanism for low-cost robotic systems due to vision sensor failure in vSLAM systems. The approach takes advantage of ROS architecture and adopts the Shannon Nyquist sampling theory to selectively sample path parameters that will be used for back travel in case of vision sensor failure. As opposed to point clouds normally used to store vSLAM data, this paper proposes to store and use lightweight variables namely distance between sampled points, velocity combinations, i.e., linear and angular velocity, sampled period, and yaw angle values to describe the robot path and reduce the memory space required to store these variables. In this study, low-cost robotic systems typically using cameras aided by proprioceptive sensors such as IMU during vSLAM activities are investigated. A demonstration is made on how the ROS architecture can be used in a scenario where vision sensing is adversely affected, resulting in mapping failure. Additionally, a recommendation is made for adoption of the approach for vSLAM platforms implemented on both ROS1 and ROS2. Furthermore, a proposal is made to add an additional layer to vSLAM systems that will be exclusively used for back travel in case of vision loss during vSLAM activities resulting in mapping failure.

In order to make a fast and accurate response to gas leakage event, e.g. gas leakage in hydrogen storage station, it is very important to identify and locate the leakage source accurately and quickly. Due to the flexibility and the adaptability of robots to harsh environments, leakage source tracing based on mobile robots has attracted more and more attention. However, the existing ground robots are limited by the ground environment and thus it is difficult to trace and locate the leakage in the complex environment with ground robots. Although unmanned aerial vehicle (UAV) can overcome the limitation of ground obstacles, there are still some problems in the accuracy and reliability of gas sampling due to the interference of flow field caused by UAV rotors to the surrounding gases. Based on computational fluid dynamic simulation, a simulation model of UAV with four rotors was established. Combined with test experiments, the influence of flow field around UAV on gas sampling under different UAV speeds, rotors assembly structures, leakage, and sampling conditions was analyzed and investigated. The optimized UAV assembly structure and gas sensor installation position were determined and verified by the simulations and experiments. The results showed that the sensor was less affected by the rotor airflow when the UAV rotor was reversely assembled and the gases were sampled above the UAV. This research can provide a guidance for gas sampling for emission source tracing with UAV for process safety management of energy gas storage.

Visual odometry (VO) is a key technology for estimating camera motion from captured images. In this paper, we propose a novel RGB-D visual odometry by constructing and matching features at the superpixel level that represents better adaptability in different environments than state-of-the-art solutions. Superpixels are content-sensitive and perform well in information aggregation. They could thus characterize the complexity of the environment. Firstly, we designed the superpixel-based feature SegPatch and its corresponding 3D representation MapPatch. By using the neighboring information, SegPatch robustly represents its distinctiveness in various environments with different texture densities. Due to the inclusion of depth measurement, the MapPatch constructs the scene structurally. Then, the distance between SegPatches is defined to characterize the regional similarity. We use the graph search method in scale space for searching and matching. As a result, the accuracy and efficiency of matching process are improved. Additionally, we minimize the reprojection error between the matched SegPatches and estimate camera poses through all these correspondences. Our proposed VO is evaluated on the TUM dataset both quantitatively and qualitatively, showing good balance to adapt to the environment under different realistic conditions.

This paper presents a decentralized, cooperative, real-time avoidance control strategy for robotic manipulators. The proposed avoidance control law builds on the concepts of artificial potential field functions and provides tighter bounds on the minimum safe distance when compared to traditional potential-based controllers. Moreover, the proposed avoidance control law is given in analytical, continuous closed form, avoiding the use of optimization techniques and discrete algorithms, and is rigorously proven to guarantee collision avoidance at all times. Examples of planar and 3D manipulators with cylindrical links under the proposed avoidance control are given and compared with the traditional approach of modeling links and obstacles with multiple spheres. The results show that the proposed avoidance control law can achieve, in general, faster convergence, smaller tracking errors, and lower control torques than the traditional approach. Furthermore, we provide extensions of the avoidance control to robotic manipulators with bounded control torques.

This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.

Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians.

Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

Over decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

In this study, we present a novel approach to generate limit cycle walking using nonlinear model predictive control (NMPC). Output-zeroing control is now widely used as a control method to limit cycle walking. This control offers strong feedback to the desired trajectory and the generation of energy-efficient and robust limit cycle walking. However, we observed that this method disables the natural dynamics of the robot, leading to problems regarding energy efficiency during walking. This study demonstrates that the energy consumption of walking using the output-zeroing control increases significantly in a disturbed environment. To overcome this limitation, the proposed approach leverages the robot’s dynamics using NMPC to achieve energy-efficient walking even in a disturbed environment. We demonstrate the practicality of the proposed method using two different simulation environments.

Unmanned aerial vehicles (UAVs) possess fast-moving abilities and have been used in various tasks in the past decades. However, their performances are still restricted by insufficient endurance and confined environments. Intuitively, combining other locomotion modes with UAVs, such as diving and driving, would be an appropriate idea to improve the robot’s adaptability and solve the endurance problem. Recently, the terrestrial/aerial hybrid robots have drawn the researchers’ eyes for their outstanding performances, which can deploy flight mode to traverse insurmountable terrains and ground mode to increase endurance and realize detailed searches. Therefore, this paper developed the autonomous quadrotor tilting hybrid robot (AQT-HR) to achieve terrestrial/aerial dual-modal mobility and verified that the robot delivers high energy efficiency. The AQT-HR can achieve flying and driving through a quadrotor tilting mechanism, which can alter one single driving force into different directions. Furthermore, the dynamic models of the hybrid robot’s aerial and ground locomotion are derived and introduced into the model-feedforward PID control algorithm for improving the robot’s flying stability. Finally, we conducted some mobility tests and experiments about traversing obstacles to demonstrate that the proposed hybrid robot can realize autonomous mode switching and perform a low energy consumption in ground movement mode.

As space missions are needed in the future, the assembly volume of the space truss will become larger and larger, and the advancing path of the on-orbit cellular robot to the mission target will become more and more complicated. If the shortest moving path cannot be found in the truss environment, the climbing time of the robot on the truss will be greatly increased. To improve the speed of the cellular robot moving to the target point on the large space truss, this paper designs a cellular robot structure and configuration suitable for climbing on the truss and uses the improved sparrow algorithm to solve the problem of robot motion path planning. By establishing a mathematical model of the space truss, the improved sparrow algorithm is used to find the shortest path between the starting point and the end point in the truss environment. Finally, the data of this algorithm are compared with the data of other algorithms. The data results show that the improved sparrow algorithm is very effective in solving the optimal path of the space truss. The improved sparrow algorithm keeps the same optimal path compared with the standard sparrow algorithm, and the overall reaction time is increased by 51.60%, and the number of effective iterations is increased by about 13.87%.

This article focuses on the luggage trolley transportation problem, an essential part of robotic autonomous luggage trolley collection. To efficiently address the nonholonomic constraints derived from the formation of two collaborative robots and a queue of luggage trolleys, we propose a comprehensive framework consisting of a global planning method and a real-time divide-and-conquer control strategy. The popular Hybrid A* algorithm generates a feasible path as the global planner. A model predictive controller is designed to track this path stably and in real time. To maintain the formation so that the whole queue of robots and luggage trolleys does not split, a safety filter that consists of a discrete-time control Lyapunov function and a decentralized control barrier function is implemented in the transportation process. Finally, we conduct real-world experiments to verify the effectiveness of the proposed method on three representative paths, and the results show that our approach can achieve robust performance. The demonstration video can be found at https://www.youtube.com/watch?v=iPiT8BfLIpU.

Mobile robots are a key component for the automation of many tasks that either require high precision or are deemed too hazardous for human personnel. One of the typical duties for mobile robots in the industrial sector is to perform trajectory tracking, which involves pursuing a specific path through both space and time. In this paper, an iterative learning-based procedure for highly accurate tracking is proposed. This contribution shows how data-based techniques, namely Gaussian process regression, can be used to tailor a motion model to a specific reoccurring reference. The procedure is capable of explorative behavior meaning that the robot automatically explores states around the prescribed trajectory, enriching the data set for learning and increasing the robustness and practical training accuracy. The trade-off between highly accurate tracking and exploration is done automatically by an optimization-based reference generator using a suitable cost function minimizing the posterior variance of the underlying Gaussian process model. While this study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. The effectiveness of this approach is validated in meaningful real-world experiments on a custom-built omnidirectional mobile robot where it is shown that explorative behavior can outperform purely exploitative approaches.

Robotic rovers equipped with articulated rocker-bogie suspension have aroused great interest after the explorations on Mars; this interest has also shifted to different types of terrestrial applications such as in the agriculture, military, and rescue fields. The suspension can be designed so that, when the rover is on flat and horizontal ground, the weight is evenly distributed among the wheels; in this way, all wheels have the same traction capability and offer the same rolling resistance. As the operating conditions vary due to sloping ground, uneven ground surface, or different payload position, the weight distribution can undergo considerable variations. This type of suspension is statically determined with respect to weight, but it is indeterminate with respect to traction forces; the traction control system aims to avoid the wheels slippage. In this paper, the traction contribution that each wheel can provide, to overcome a step obstacle, is shown. Furthermore, the possibility of regulating the distribution of vertical loads among the wheels adopting a torsion spring, with adjustable preload, arranged between rocker and bogie, is evaluated. A suitable spring preload facilitates the initial phase of the obstacle overcoming if the rover advances with the bogie forward. Numerical simulations show that to increase the possibility of overcoming an obstacle it is sufficient for the spring preload to reduce the vertical load on the front wheel; in any case, a higher load variation would not be advisable as it could involves an excessive load difference among the wheels.

A robotic system constructed as a wheeled inverted pendulum (WIP) serves as an impressive demonstrator, since this kind of system is inherently nonlinear, unstable, and nonminimum phase. These properties may pose several difficulties, when it comes to control and trajectory planning. This paper shows a method for deriving a highly dynamic trajectory compliant with the system dynamics by means of solving an optimal control problem (OCP) using multiple shooting. The assumed task includes that the WIP should pass a height-restricting barrier. This can be achieved by leaning back or forth, in order to reduce the overall height of the WIP. The constraints inherent to the definition of this trajectory are nonconvex due to the shape of the robot. The constraint functions have a local minimum in an infeasible region. This can cause problems when the initial guess is within this infeasible region. To overcome this, a multistage approach is proposed for this special OCP to evade the infeasible local minimum. After solving four stages of subsequent optimization problems, the optimal trajectory is obtained and can be used as feedforward for the real system.

Thanks to recent developments in service robotics technologies, precision agriculture (PA) is becoming an increasingly prominent research field, and several studies were made to present and outline how the use of mobile robotic systems can help and improve farm production. In this paper, the integration of a custom-designed mobile base with a commercial robotic arm is presented, showing the functionality and features of the overall system for crop monitoring and sampling. To this aim, the motion planning problem is addressed, developing a tailored algorithm based on the so-called manipulability index, that treats the base and robotic arm mobility as two independent degrees of motion; also developing an open source closed-form inverse kinematics algorithm for the kinematically redundant manipulator. The presented methods and sub-system, even though strictly related to a specific mobile manipulator system, can be adapted not only to PA applications where a mobile manipulator is involved but also to the wider field of assistive robotics.

The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.

Mobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.

In this paper, we propose a set of robust training methods for deep reinforcement learning to transfer learning acquired in one control task to a set of previously unseen control tasks. We improve generalization in commonly used transfer learning benchmarks by a novel sample elimination technique, early stopping, and maximum entropy adversarial reinforcement learning. To generate robust policies, we use sample elimination during training via a method we call strict clipping. We apply early stopping, a method previously used in supervised learning, to deep reinforcement learning. Subsequently, we introduce maximum entropy adversarial reinforcement learning to increase the domain randomization during training for a better target task performance. Finally, we evaluate the robustness of these methods compared to previous work on simulated robots in target environments where the gravity, the morphology of the robot, and the tangential friction coefficient of the environment are altered.

Mobile robotic systems are used in a wide range of applications. Especially in the assistive field, they can enhance the mobility of the elderly and disable people. Modern robotic technologies have been implemented in wheelchairs to give them intelligence. Thus, by equipping wheelchairs with intelligent algorithms, controllers, and sensors, it is possible to share the wheelchair control between the user and the autonomous system. The present research proposes a methodology for intelligent wheelchairs based on head movements and vector fields. In this work, the user indicates where to go, and the system performs obstacle avoidance and planning. The focus is developing an assistive technology for people with quadriplegia that presents partial movements, such as the shoulder and neck musculature. The developed system uses shared control of velocity. It employs a depth camera to recognize obstacles in the environment and an inertial measurement unit (IMU) sensor to recognize the desired movement pattern measuring the user’s head inclination. The proposed methodology computes a repulsive vector field and works to increase maneuverability and safety. Thus, global localization and mapping are unnecessary. The results were evaluated by simulated models and practical tests using a Pioneer-P3DX differential robot to show the system’s applicability.