Showing papers on "Mobile robot published in 2018"
TL;DR: DS-SLAM as discussed by the authors combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments.
Abstract: Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: this https URL
401 citations
01 Oct 2018
TL;DR: DS-SLAM as mentioned in this paper combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments.
Abstract: Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments.
356 citations
Book•
13 Nov 2018
TL;DR: The book begins with a study of mobile robot drives and corresponding kinematic and dynamic models, and discusses the sensors used in mobile robotics, and examines a variety of model-based, model-free, and vision-based controllers with unified proof of their stabilization and tracking performance.
Abstract: Introduction to Mobile Robot Control provides a complete and concise study of modeling, control, and navigation methods for wheeled non-holonomic and omnidirectional mobile robots and manipulators. The book begins with a study of mobile robot drives and corresponding kinematic and dynamic models, and discusses the sensors used in mobile robotics. It then examines a variety of model-based, model-free, and vision-based controllers with unified proof of their stabilization and tracking performance, also addressing the problems of path, motion, and task planning, along with localization and mapping topics. The book provides a host of experimental results, a conceptual overview of systemic and software mobile robot control architectures, and a tour of the use of wheeled mobile robots and manipulators in industry and society. Introduction to Mobile Robot Control is an essential reference, and is also a textbook suitable as a supplement for many university robotics courses. It is accessible to all and can be used as a reference for professionals and researchers in the mobile robotics field. It clearly and authoritatively presents mobile robot concepts. Features: richly illustrated throughout with figures and examples; key concepts demonstrated with a host of experimental and simulation examples; and no prior knowledge of the subject is required; each chapter commences with an introduction and background.
228 citations
TL;DR: This paper proposes a 3D printing system that employs multiple mobile robots printing concurrently a large, single-piece, structure, and is the first physical demonstration of large-scale, concurrent,3D printing of a concrete structure by multiple mobile Robots.
203 citations
TL;DR: The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot.
Abstract: Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.
199 citations
TL;DR: The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends.
Abstract: Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.
181 citations
01 Oct 2018
TL;DR: Two state of the art models are compared for object detection, Single Shot Multi-Box Detector with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2, and result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection.
Abstract: Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement in deep neural networks in image processing, classifying and detecting the object accurately is now possible. In this paper, Convolutional Neural Networks (CNN) is used to detect objects in the environment. Two state of the art models are compared for object detection, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. Result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection.
171 citations
TL;DR: A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots.
Abstract: This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking , when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.
168 citations
TL;DR: This article summarizes new aerial robotic manipulation technologies and methods-aerial robotic manipulators with dual arms and multidirectional thrusters-developed in the AEROARMS project for outdoor industrial inspection and maintenance (I&M).
Abstract: This article summarizes new aerial robotic manipulation technologies and methods-aerial robotic manipulators with dual arms and multidirectional thrusters-developed in the AEROARMS project for outdoor industrial inspection and maintenance (IaM).
167 citations
TL;DR: Control techniques for cooperative mobile robots monitoring multiple targets are reviewed for the first time, and the five major elements that characterize this problem are identified, namely, the coordination method, the environment, the target, the robot and its sensor(s).
Abstract: The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.
161 citations
TL;DR: A reinforcement learning strategy for manipulation and grasping of a mobile manipulator is described, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations.
Abstract: It is important for humanoid-like mobile robots to learn the complex motion sequences in human–robot environment such that the robots can adapt such motions. This paper describes a reinforcement learning (RL) strategy for manipulation and grasping of a mobile manipulator, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations. Two hierarchies plannings have been considered in the proposed strategy: 1) high-level online redundancy resolution based on the neural-dynamic optimization algorithm in operational space; and 2) low-level RL in joint space. At this level, the dynamic movement primitives have been considered to model and learn the joint trajectories, and then the RL is employed to learn the trajectories with uncertainties. Experimental results on the developed humanoidlike mobile robot demonstrate that the presented approach can suppress the uncertain external perturbations.
TL;DR: The present research on mobile robotics addresses the problems which are mainly on path planning algorithm and optimization in static as well as dynamic environments with a focus on meta-heuristic methods.
TL;DR: An autonomous mobile robot which navigates a scan site based on a continuously updated point cloud map using the 2D Hector Simultaneous Localization and Mapping (SLAM) technique to estimate real-time positions and orientations of the robot in the x-y plane.
21 Jun 2018
TL;DR: A novel terrain mapping method, which bases on proprioceptive localization from kinematic and inertial measurements only, which yields a probabilistic terrain estimate as a grid-based elevation map including upper and lower confidence bounds.
Abstract: Mobile robots build on accurate, real-time mapping with onboard range sensors to achieve autonomous navigation over rough terrain. Existing approaches often rely on absolute localization based on tracking of external geometric or visual features. To circumvent the reliability issues of these approaches, we propose a novel terrain mapping method, which bases on proprioceptive localization from kinematic and inertial measurements only. The proposed method incorporates the drift and uncertainties of the state estimation and a noise model of the distance sensor. It yields a probabilistic terrain estimate as a grid-based elevation map including upper and lower confidence bounds. We demonstrate the effectiveness of our approach with simulated datasets and real-world experiments for real-time terrain mapping with legged robots and compare the terrain reconstruction to ground truth reference maps.
21 May 2018
TL;DR: In this article, the authors adopt a generative adversarial imitation learning (GAIL) strategy to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner.
Abstract: We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.
TL;DR: The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot and account for a grid-based map representation of the environment and solve an optimization in the space of control velocities with additional constraints.
Abstract: In this paper, we present a method, namely $\epsilon$ CCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for smooth and safe control. The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot and account for a grid-based map representation of the environment. The method is best suited for large multirobot settings, including heterogeneous teams of robots, in which computational complexity is of paramount importance and the robots interact with one another. In particular, we consider a set of motion primitives for the robot and solve an optimization in the space of control velocities with additional constraints. Additionally, we propose a cooperative approach to compute safe velocity partitions in the distributed case. We describe several instances of the method for distributed and centralized operation and formulated both as convex and nonconvex optimizations. We compare the different variants and describe the benefits and tradeoffs both theoretically and in extensive experiments with various robotic platforms: robotic wheelchairs, robotic boats, humanoid robots, small unicycle robots, and simulated cars.
TL;DR: The proposed controller solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies, and the performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.
TL;DR: A novel method for human–robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue, is proposed, which is based on the human muscle activity measured by the electromyography.
Abstract: In this paper, we propose a novel method for human---robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue. The robot starts as a follower and imitates the human. As the collaborative task is performed under the human lead, the robot gradually learns the parameters and trajectories related to the task execution. In the meantime, the robot monitors the human fatigue during the task production. When a predefined level of fatigue is indicated, the robot uses the learnt skill to take over physically demanding aspects of the task and lets the human recover some of the strength. The human remains present to perform aspects of collaborative task that the robot cannot fully take over and maintains the overall supervision. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed approach with experiments on real-world co-manipulation tasks: material sawing and surface polishing.
07 Mar 2018
TL;DR: This work presents a human-aware robotic system with single-axis mobility that incorporates both predictions of human motion and planning in time to execute efficient and safe motions during automotive final assembly.
Abstract: Introducing mobile robots into the collaborative assembly process poses unique challenges for ensuring efficient and safe human–robot interaction. Current human–robot work cells require the robot to cease operating completely whenever a human enters a shared region of the given cell, and the robots do not explicitly model or adapt to the behavior of the human. In this work, we present a human-aware robotic system with single-axis mobility that incorporates both predictions of human motion and planning in time to execute efficient and safe motions during automotive final assembly. We evaluate our system in simulation against three alternative methods, including a baseline approach emulating the behavior of standard safety systems in factories today. We also assess the system within a factory test environment. Through both live demonstration and results from simulated experiments, we show that our approach produces statistically significant improvements in quantitative measures of safety and fluency of interaction.
TL;DR: A global overview of mobile robot control and navigation methodologies developed over the last decades, including the industrial, service, medical, and socialization sectors, is provided.
Abstract: The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.
TL;DR: An improved ant colony algorithm is introduced that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy.
Abstract: Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and b...
TL;DR: Under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive constant and zero, respectively, over the iteration domain, beyond a small initial time interval in each iteration.
TL;DR: A novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization (PSO).
Abstract: As a challenging optimization problem, path planning for mobile robot refers to searching an optimal or near-optimal path under different types of constrains in complex environments. In this paper, a self-adaptive learning particle swarm optimization (SLPSO) with different learning strategies is proposed to address this problem. First, we transform the path planning problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: path length, collision risk degree and smoothness. Then, a novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization (PSO). Moreover, in order to enhance the feasibility of the generated paths, we further apply the new bound violation handling schemes to restrict the velocity and position of each particle. Finally, experiments respectively with a simulated robot and a real robot are conducted and the results demonstrate the feasibility and effectiveness of SLPSO in solving mobile robot path planning problem.
TL;DR: A novel learning algorithm called “Reset-free Trial-and-Error” (RTE) is introduced that breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account.
TL;DR: This paper proposes a novel robust zeroing neural-dynamics approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators with bounded or zero-oriented steady-state position error and investigates four common forms of time-varying disturbances suppressed by the proposed RZND model.
Abstract: This paper proposes a novel robust zeroing neural-dynamics (RZND) approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators. Unlike existing works based on the assumption that neural network models are free of external disturbances, four common forms of time-varying disturbances suppressed by the proposed RZND model are investigated in this paper. In addition, theoretical analyses on the antidisturbance performance are presented in detail to prove the effectiveness and robustness of the proposed RZND model with time-varying disturbances suppressed for solving the inverse kinematics problem of mobile robot manipulators. That is, the RZND model converges toward the exact solution of the inverse kinematics problem of mobile robot manipulators with bounded or zero-oriented steady-state position error. Moreover, simulation studies and comprehensive comparisons with existing neural network models, e.g., the conventional Zhang neural network model and the gradient-based recurrent neural network model, together with extensive tests with four common forms of time-varying disturbances substantiate the efficacy, robustness, and superiority of the proposed RZND approach as well as its time-varying disturbances suppression model for solving the inverse kinematics problem of mobile robot manipulators.
TL;DR: In this article, an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration is used for trajectory tracking, and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking.
Abstract: This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.
TL;DR: A fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented, and a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones.
Abstract: This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.
TL;DR: A wheeled robot using a LM droplet as the core of the driving system is developed that enables it to move outside liquid environment and is envisaged to have the potential to expand current research on LM-based actuators to realize future complex robotic systems.
Abstract: The controlled actuation of gallium liquid-metal (LM) alloys has presented new and exciting opportunities for constructing mobile robots with structural flexibility. However, the locomotion of current LM-based actuators often relies on inducing a gradient of interfacial tension on the LM surface within electrolytes, which limits their application outside a liquid environment. In this work, a wheeled robot using a LM droplet as the core of the driving system is developed that enables it to move outside liquid environment. The LM droplet inside the robot is actuated using a voltage to alter the robot's center of gravity, which in turn generates a rolling torque and induces continuous locomotion at a steady speed. A series of experiments is carried out to examine the robot's performance and then to develop a dynamic model using the Lagrange method to understand the locomotion. An untethered and self-powered wheeled robot that utilizes mini-lithium-batteries is also demonstrated. This study is envisaged to have the potential to expand current research on LM-based actuators to realize future complex robotic systems.
TL;DR: Results demonstrate a feasible, fast, oscillation-free and collision-free path planning of the proposed method, which is practically feasible that can be applied to both static and dynamic environments.
Abstract: This paper deals with the mobile robots path planning problem in the presence of scattered obstacles in a visually known environment. Utilizing the theory of charged particles’ potential fields and inspired by a key idea of the authors’ recent work, an optimization based approach is proposed to obtain an optimal and robust path planning solution. By assigning a potential function for each individual obstacle, the interaction of all scattered obstacles are integrated in a scalar potential surface (SPS) which strongly depends on the physical features of the mobile robot and obstacles. The optimum path is then obtained from a cost function optimization by attaining a trade-off between traversing the shortest path and avoiding collisions, simultaneously. Hence, irrespective of any physical constraints on the obstacles/mobile-robot and the adjacency of the target to the obstacles, the achieved results demonstrate a feasible, fast, oscillation-free and collision-free path planning of the proposed method. Utilizing a scalar decision variable makes it extremely simple in terms of mathematical computations and thus practically feasible that can be applied to both static and dynamic environments. Finally, simulation results verified the performance and fulfillment of the mentioned objectives of the approach.
TL;DR: The coordination error between a pair of interacting robots is explicitly used in the control design to weaken the dependence on the estimated state of the leader, and enhance the decentralized nature of the proposed control scheme.
Abstract: The problem of the leader-following formation control of nonholonomic mobile robots is addressed in this paper. A distributed formation control strategy using explicitly the coordination errors among robots is proposed without assuming that each follower robot knows the full state of the leader. First, a distributed estimation law is proposed for each follower robot to estimate the states, including the position, orientation, and linear velocity of the leader. The distributed formation control law is then designed based on the estimated states of the leader, and the neighborhood formation tracking error. Under some mild assumptions on the interaction graph among the leader and the follower robots, and the velocity of the leader, asymptotic convergence of formation tracking errors to zero can be achieved. Finally, some numerical simulations and experiments on a group of nonholonomic mobile robots are presented to demonstrate the effectiveness of the proposed strategy. Note to Practitioners —The motivation of this paper is to investigate a practical control strategy for the leader-following formation of multiple autonomous mobile robots subjected to nonholonomic constraints. In most of the existing leader-following formation control schemes for nonholonomic mobile robots, having access to the full state of the leader is a requirement. However, due to limitations in communication bandwidth and range, it is reasonable to assume that the information of the leader is available only to a subset of followers. Hence, this paper suggests a new distributed leader-following formation control strategy based on the distributed estimation of the leader’s states. Moreover, the coordination error between a pair of interacting robots is explicitly used in the control design to weaken the dependence on the estimated state of the leader, and enhance the decentralized nature of the proposed control scheme. The stability and convergence of the system are analyzed mathematically and the experiment using unicycles provides promising results. In ongoing research, we are addressing the issues of collision avoidance and communication delays to provide more realistic setup for the industrial applications of multivehicle systems.