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Motion planning is a core issuein the field of robotic control, which directly affects the programming efficiency of robots. In this article, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking, and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining “virtual fences,” which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into a quadratic programming (QP) one, in which the redundant degrees of freedom are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the abilityto remove physical fences, quick rearrangement, and performance optimization. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called Highlight Experience Replay and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages. [ABSTRACT FROM AUTHOR]

The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention towards road safety in recent days. Particularly, a higher number of road accidents occurs when the AV tries to overtake or change the lane. To cut down the number of accidents and improve traffic reliability, the AV should be capable of making intelligent decisions and communicating those to other AVs. Therefore, in this paper, a Cooperative Collision avoidance scheme for AVs at Overtaking and Lane Changing maneuver (CCAV-OLC) is proposed. The Inverse Reinforcement Learning (IRL) in the CCAV-OLC scheme, processes on the given number of expert demonstrations for automatically acquiring the reward function, and thereby imitating actual human driving strategy and decisions. However, the adaptability of IRL to a high-dimensional AV environment restricts the performance of the CCAV-OLC scheme. To overcome this, the IRL in CCAV-OLC leverages the Gaussian Process (GP) regression model (IRL-GP), which enables data-efficient Bayesian prediction even when the number of demonstrations is very low. After taking intelligent decisions in overtaking and lane changing maneuvers, the AVs cooperatively communicate and exchange the decisions with each other by 6th Generation Vehicle-to-Everything (6G-V2X) communications, which further improves the accuracy and lessens the time taken for making optimal decisions. The experimental results show that the AVs clone the expert's optimal driving strategy and avoid the collisions to a greater extent. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reinforcement learning approach of collision avoidance and investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks. Specifically, each UAV takes charge of delivering objects in the forward path and collecting data from heterogeneous ground IoT devices in the backward path. We adopt reinforcement learning for assisting UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance. In addition, for each UAV, we use optimization theory to find out a shortest backward path that assures data collection from all associated IoT devices. To obtain an optimal visiting order for IoT devices, we formulate and solve a no-return traveling salesman problem. Given a visiting order, we formulate and solve a sequence of convex optimization problems to obtain line segments of an optimal backward path for heterogeneous ground IoT devices. We use analytical results and simulation results to justify the usage of the proposed approach. Simulation results show that the proposed approach is superior to a number of alternative approaches. [ABSTRACT FROM AUTHOR]

This article proposes a targeting posture control approach with dynamic obstacle avoidance of differential-drive wheeled mobile robot (WMR) systems in the presence of unknown skidding, slipping, input disturbances, model uncertainties, and torque saturation. First, a nonlinear model predictive control (NMPC) scheme is presented to generate a feasible trajectory from a starting posture to a targeting posture where dynamic obstacles in the environment and physical constraints of the robots are considered. Second, a robust virtual control law at the kinematic level is introduced for the robots to follow the trajectory. Third, taking the consideration of the unknown skidding, slipping, input disturbances, and model uncertainties in the dynamic model being lumped as a total disturbance, which is estimated by the linear extended state observer (LESO), a disturbance compensation-based saturation controller is designed to make the real velocity of the robot converge to the virtual velocity command. Finally, the effectiveness of the proposed control strategy is verified by simulation results. [ABSTRACT FROM AUTHOR]

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA. [ABSTRACT FROM AUTHOR]

This article proposes an equal-distance surrounding control method for second-order nonlinear multiagent systems (MASs) to encircle multiple moving targets with guaranteed collision avoidance. First, a distributed estimator is developed to approximate the center of moving targets. Then, an adaptive distributed control law is designed for the MASs to accomplish equal-distance surrounding collaboratively. In particular, conditions for assuring asymptotical stability for the closed-loop MAS are derived. Finally, experimental results with unmanned surface vessels are reported to substantiate the effectiveness of the proposed coordinated surrounding control method. [ABSTRACT FROM AUTHOR]

This article addresses the distributed path maneuvering of underactuated unmanned surface vehicles (USVs) with collision avoidance and connectivity preservation. The USVs are guided by the multiple virtual leaders moving along the multiple parameterized paths and only a fraction of USVs have access to the virtual leaders. An observer-based finite-time control method is proposed to achieve a containment formation. Specifically, a finite-time extended state observer is employed to recover the unmeasured linear/angular velocities and estimate the total disturbances consisting of model uncertainties as well as ocean disturbances at first. Then, observer-based finite-time guidance laws using the information of neighbors are designed based on a containment scheme. An artificial potential field is incorporated into the distributed guidance law design to avoid collision and preserve connectivity. Finally, antidisturbance kinetic control laws are devised based on the finite-time convergent observers and nonlinear tracking differentiators. It is proven that all error signals in the closed-loop system are ultimately uniformly bounded, and the distributed path maneuvering formation pattern can be achieved in a finite time when USVs outside the collision avoidance and connectivity preservation region. Simulation results are given to verify the effectiveness of the proposed output feedback control method for the distributed path maneuvering of multiple USVs with position-yaw measurements only. [ABSTRACT FROM AUTHOR]

A novel passing priority allocation method, based on the collision avoidance urgency, used for a formation of UAVs in a serried obstacle laden environment, is presented in this article. The method employs the collision cone and the prediction of the collision time to evaluate the urgency of imminent collision of each UAV, so as to establish a primary criterion for the priority allocation. And, the priority update policy is also innovative designed to alleviate burdens on the instability of frequent shifty context. This article exploits the potential field in a cooperative way to plan the path for each UAV, steering the UAVs to a given 3-D position, which combines the adaptive priorities to guide the UAVs passing through the barriers in turn. Numerical simulations and semi-physical experiments in designed outdoor condition are carried out to compare the performance of the proposed adaptive priorities with the regular-used default priorities. The total passing time and the energy cost of the semi-physical tests provide the promising validation in terms of the efficiency and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle. [ABSTRACT FROM AUTHOR]

In this article, we present a distributed formation control strategy for agents with a variety of dynamics to achieve a desired planar formation. Our approach is based on the barycentric-coordinate-based (BCB) control, which is fully distributed, does not require interagent communication or a common sense of orientation, and can be implemented using relative position measurements acquired by agents in their local coordinate frames. This removes the need for global positioning or alignment of local coordinate frames, which are required across several existing strategies. We show how the BCB control for agents with the simplest dynamical model, i.e., the single-integrator dynamics, can be extended to agents with higher order dynamics such as quadrotors, and nonholonomic agents such as unicycles and cars. Specifically, our extension preserves the desired convergence and robustness guarantees of the BCB approach and is provably robust to saturations in the input and unmodeled linear actuator dynamics for unicycle and car agents. We further show that under our proposed BCB control design, the agents can move along a rotated and scaled control direction without affecting the convergence to the desired formation. This observation is used to design a fully distributed collision avoidance strategy, which is often not considered in the formation control literature. We demonstrate the proposed approach in simulations and further present a distributed robotic platform to test the strategy experimentally. Our experimental platform consists of off-the-shelf equipment that can be used to test and validate other multiagent algorithms. The code and implementation instructions for this platform are available online. [ABSTRACT FROM AUTHOR]

A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation. It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space. It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate. The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation. In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents. By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions. Distributed control rules are also defined to confine the motion space of each agent for collision avoidance. It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment. In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation. Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density. [ABSTRACT FROM AUTHOR]

Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape nor that the vehicle follows a desired speed trajectory. Instead, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component that cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations and through full-scale experiments aboard the R/V Gunnerus. [ABSTRACT FROM AUTHOR]

In this article, an infinite-horizon optimal regulation problem is considered for a control-affine nonlinear autonomous agent subject to input constraints in the presence of dynamic avoidance regions. A local model-based approximate dynamic programming method is implemented to approximate the value function in a local neighborhood of the agent. By performing local approximations, prior knowledge of the locations of avoidance regions is not required. To alleviate the a priori knowledge of the number of avoidance regions in the operating domain, an extension is provided that modifies the value function approximation. The developed feedback-based motion planning strategy guarantees uniformly ultimately bounded convergence of the approximated control policy to the optimal policy while also ensuring the agent remains outside avoidance regions. Simulations are included to demonstrate the preliminary development for a kinematic unicycle and generic nonlinear system. Results from three experiments are also presented to illustrate the performance of the developed method, where a quadcopter achieves approximate optimal regulation while avoiding three mobile obstacles. To demonstrate the developed method, known avoidance regions are used in the first experiment, unknown avoidance regions are used in the second experiment, and an unknown time-varying obstacle directed by a remote pilot is included in the third experiment. [ABSTRACT FROM AUTHOR]

Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement-based solutions suffer either from restricted fields-of-view or significant computational complexity and are, hence, not easily portable to small platforms. In order to overcome these drawbacks, in this paper, two novel bioinspired approaches are proposed to extract information about small-field objects contained in planar optic flow. The first approach, which is analogous to the small-field extraction process hypothesized to occur in the lobula plate of the fly visual system, is based on the Fourier residual analysis of instantaneous optic flow. Alternatively, the flow-of-flow method is the engineering analogue of the small-field extraction process thought to occur in the fruit-fly's medulla, and extracts high-frequency content of optic flow by means of an elementary motion detector array. Both approaches extract instantaneous relative range and bearing of small-field obstacles from planar optic flow in a local environment characterized by small and wide-field obstacles, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bioinspired approach is computationally efficient, which renders extraction of vehicle velocity and local environment structure superfluous, and thus, serves as a robust, reflexive solution to the problem of small-object detection, and avoidance for small autonomous robots. [ABSTRACT FROM AUTHOR]

This paper presents a trajectory planning strategy for collision avoidance in unmanned aerial vehicles. First, a varying cell strategy is proposed to integrate aerodynamic constraints into trajectory planning. Basic avoidance actions in the varying cell strategy are adapted accordingly to go through different cells, enabling more flexible avoidance maneuvers. Second, given the limited decision-making time involved, offline and online path planning methods are developed to increase the convergence rate. Finally, Monte Carlo simulations demonstrate that the proposed method satisfies aerodynamic constraints, while both the convergence and collision avoidance rates are better than that achieved by fixed cell-based methods. [ABSTRACT FROM AUTHOR]

This paper presents a control approach for an overground lower limb exoskeleton that is intended to provide guidance and assistance to poorly ambulatory individuals during walking without unduly interfering with their ability to maintain balance. The control approach achieves these objectives by emulating a viscous flow field acting on the lower limb joints. The extent to which the control approach achieves the objectives was assessed in experiments, conducted on five healthy subjects, comparing guidance and disturbance characteristics of the velocity-based controller to a potential-field-based controller. Results show that the flow controller provides a combination of lower guidance error and lower disturbance to the user, relative to the potential-field-based controller. The paper also discusses various potentially beneficial characteristics of the flow controller, such as first-order homogeneous behavior, implicitly combined guidance and assistance behaviors, and improved directionality in error correction relative to a potential-field-based controller. [ABSTRACT FROM AUTHOR]

This paper describes a set of software modules and algorithms for obstacle avoidance. The approach described here is designed to work in conjunction with any path planning method that generates a viable sequence of waypoints, with obstacle avoidance regarded as a local deviation of the planned path of points. The helm architecture used here will temporarily spawn a new obstacle avoidance behavior for each near detected obstacle, and generate a maneuvering objective function to avoid the obstacle. Once the vehicle passes the obstacle, the helm deletes the behavior and frees its memory space. The helm, obstacle manager, and obstacle avoidance behavior are described here. This software arrangement was used by the MIT 2014 RobotX team, and will be included in the next software release of the open source MOOS-IvP marine autonomy project. [ABSTRACT FROM AUTHOR]

Two missile guidance algorithms are proposed for the intercept and for the rendezvous of a maneuvering target, while avoiding a static obstacle by a specified avoidance distance. The guidance algorithms are derived as solutions of a linear quadratic optimal control problem and they are expressed as formulas reminiscent of the classical (augmented) proportional navigation type guidance laws. These formulas include correction terms that are responsible for obstacle avoidance and are parameterized by the minimum avoidance distance. Simulation results are used to demonstrate the performance of the two algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a sophisticated vision-aided flocking system for unmanned aerial vehicles (UAVs), which is able to operate in GPS-denied unknown environments for exploring and searching missions, and also able to adopt two types of vision sensors, day and thermal cameras, to measure relative motion between UAVs in different lighting conditions without using wireless communication. In order to realize robust vision-aided flocking, an integrated framework of tracking-learning-detection on the basis of multifeature coded correlation filter has been developed. To achieve long-term tracking, a redetector is trained online to adaptively reinitialize target for global sensing. An advanced flocking strategy is developed to address the autonomous multi-UAVs' cooperative flight. Light detection and ranging (LiDAR)-based navigation modules are developed for autonomous localization, mapping, and obstacle avoidance. Flight experiments of a team of UAVs have been conducted to verify the performance of this flocking system in a GPS-denied environment. The extensive experiments validate the robustness of the proposed vision algorithms in challenging scenarios. [ABSTRACT FROM AUTHOR]

This paper addresses formation control with obstacle avoidance problem for a class of second-order stochastic nonlinear multiagent systems under directed topology. Different with deterministic multiagent systems, stochastic cases are more practical and challenging because the exogenous disturbances depicted by the Wiener process are considered. In order to achieve control objective, both the leader-follower formation approach and the artificial potential field (APF) method are combined together, where the artificial potential is utilized to solve obstacle avoidance problem. For obtaining good system robustness to the undesired side effects of the artificial potential, $H_\infty$ analysis is implemented. Based on the Lyapunov stability theory, it is proven that control objective can be achieved, of which obstacle avoidance is proven by finding an energy function satisfying that its time derivative is positive. Finally, a numerical simulation is carried out to further demonstrate the effectiveness of the proposed formation schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a control method for leader–follower navigation in obstacle environments while preserving sensing network connectivity without data transmission between robots. Unlike most connectivity-preserving algorithms, the control input is determined in such a way as to not only guarantee connectivity preservation and collision avoidance, but also to ensure that input constraints are not violated at each time step. We also introduce a simple rule for changing network topology depending on environments such that some sensing links are deactivated in order to pass through narrow spaces, while active links are increased in free spaces to keep the group as cohesive as possible. The effectiveness of the proposed method is demonstrated in simulations and experiments. [ABSTRACT FROM AUTHOR]

Smart healthcare, instead of traditional healthcare, has attracted tremendous attention all over the world, and calls for integrated design bringing together interdisciplinary technological approaches and solutions with the aim of supporting affordable and high-quality patient care. With the increasing number of visually impaired people, including the blind, the elderly, and patients with eye diseases, it is vital to help them explore their outside environment with the assistance of smart technologies so that they can adapt to the environment more easily. The smart indoor anti-collision system is one typical application. Previous works normally use video monitors or deployed sensors to provide outside environment information for visually impaired people. However, these methods suffer from privacy disclosure and inconvenience. In this article, we present IAAS, a smart indoor anti-collision system based on RFID, which identifies and tracks passive RFID tags by analyzing the received backscatter signals. We extract RSSI based on the LWLR algorithm and phase profiles as fingerprints to help the user guide from obstacles without observations of eyes. Experiments are conducted to verify our system, and results show that IAAS could achieve high accuracy of 94 percent in obstacle avoidance. [ABSTRACT FROM AUTHOR]

One of the primary challenges for autonomous robotics in uncertain and dynamic environments is planning and executing a collision-free path. Hybrid dynamic obstacles present an even greater challenge as the obstacles can change dynamics without warning and potentially invalidate paths. Artificial potential field (APF)-based techniques have shown great promise in successful path planning in highly dynamic environments due to their low cost at runtime. We utilize the APF framework for runtime planning but leverage a formal validation method, Stochastic Reachable (SR) sets, to generate accurate potential fields for moving obstacles. A small number of SR sets are computed a priori, then used to generate a potential field that represents the obstacle's stochastic motion for online path planning. Our method is novel and scales well with the number of obstacles, maintaining a relatively high probability of reaching the goal without collision, as compared to other traditional Gaussian APF methods. Here, we demonstrate our method with up to 900 hybrid dynamic obstacles and show that it outperforms the traditional Gaussian APF method by up to 60% in the holonomic case and up to 20% in the unicycle case. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid multi-objective scheme is proposed to complete simultaneously four objectives, i.e., the specified primary task for the end-effector, obstacle avoidance, joint-physical limits avoidance, and repetitive motion of redundant robot manipulators. In addition, corresponding theoretical analysis is given, which guarantees the validity of the proposed scheme. Then, the proposed hybrid multi-objective scheme is reformulated as a dynamical quadratic program (DQP) problem. The optimal solution of the DQP problem is found by the PLPE (piecewise-linear projection equation) neural network, i.e., PLPENN, and also by the corresponding numerical algorithm implemented on the computer. Furthermore, simulation and comparison based on a six-link planar redundant robot manipulator substantiate the effectiveness and accuracy of the proposed scheme. At last, a hardware experiment is conducted on a six-link physical robot manipulator system, which substantiates the physical realizability, operational stability, and safety of the proposed hybrid multi-objective scheme. [ABSTRACT FROM PUBLISHER]

Various obstacle avoidance (OA) methods with the monocular vision have been studied to assist visually impaired people. Recently, a method that effectively locates obstacles at a risk of collision using the shape variation of a grid, called deformable grid (DG), was introduced [11]. To further improve the detection performance of the previous work, a vertex deformation function representing the displacement of each vertex in the DG is firstly defined employing the perspective projection geometry. Then, the collision risk is accurately estimated based on the shape variation of the DG that is measured using the vertex deformation function. Experimental results show that the proposed method outperforms other conventional methods as well as the previous work in terms of both the accuracy and processing time, and clearly detects obstacles that present a risk of collision in a variety of scenarios. This method is suitable for application to electronic travel aid (ETA) systems using consumer devices, such as smart-phones, tablets, and wearable devices. [ABSTRACT FROM PUBLISHER]

Most existing studies of multiple mobile robots assume that robots can distinguish between other robots and obstacles. However, if a flocking algorithm was available that did not require this ability, easier implementation could be expected. In this brief, we propose a flocking algorithm that does not distinguish between a robot and an obstacle. In other words, all detected objects are regarded as obstacles in the proposed algorithm. Thus, velocity information on neighboring robots is not required. We also show that the proposed algorithm maintains the desired properties of existing flocking algorithms, even though only limited information is used in the proposed algorithm. Furthermore, unlike many previous studies, the effectiveness of the algorithm is demonstrated not only by simulations, but also in real robot experiments. In the experiments, neighboring robots and obstacles are detected using only laser range finders, since the robots do not need to distinguish between another robot and an obstacle. [ABSTRACT FROM PUBLISHER]

This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions. The goal of the controller is to track the centerline of the roadway while avoiding obstacles. An outer-loop nonlinear model predictive control is adopted for generating the collision-free trajectory with the resultant input based on a simplified vehicle model. The optimization is solved through the generalized minimal residual method augmented with a continuation method. A sufficient condition to overcome limitations associated with continuation methods is introduced. The inner loop is a simple linear feedback controller based on an optimal preview distance. Simulation results illustrate the effectiveness of the approach. These are bolstered by scaled-vehicle experimental results. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) operate in unknown underwater environments and must be able to avoid submerged obstacles such as cliffs, wrecks, and seabed changes. This paper proposes a methodology for obstacle avoidance by AUVs that are equipped with forward-looking sonars (FLSs). The data collected from two FLSs placed in horizontal and vertical orientation are processed in real time to provide obstacle detection information in the $xz$- and $xy$-planes, respectively. Due to the necessity of maintaining constant height when employing sidescan sonar (SSS) and lower energy consumption, horizontal avoidance maneuvers are preferred over vertical ones. For the horizontal obstacle avoidance, a two-layer obstacle avoidance algorithm (OAA) contains two different processes, an OAA based on Bandler and Kohout (BK) product of fuzzy relation used as a preplanning method and a reactive approach based on potential field and edge detection methods. The preplanning technique has a clear advantage on a line segment of the path, and the reactive method is more efficient on a curved segment. In case a horizontal approach cannot find a path to safely avoid the obstacle, the reactive vertical approach is activated. A seabed gradient detection technique that allows prediction of seabed and altitude changes up to 40 m ahead of the AUV is presented. Simulation and experimental results clearly demonstrate that the proposed methodology enables AUVs to navigate safely through obstacles and provide crucial information about the seafloor terrain changes. [ABSTRACT FROM PUBLISHER]

This paper introduces a motion-planning framework for a hybrid system with general continuous dynamics to satisfy a temporal logic specification consisting of cosafety and safety components in a partially unknown environment. The framework employs a multilayered synergistic planner to generate trajectories that satisfy the specification and adopt an iterative replanning strategy to deal with unknown obstacles. When the discovery of an obstacle renders the specification unsatisfiable, a division between the constraints in the specification is considered. The cosafety component of the specification is treated as a soft constraint, whose partial satisfaction is allowed, while the safety component is viewed as a hard constraint, whose violation is forbidden. To partially satisfy the cosafety component, inspirations are taken from indoor-robotic scenarios, and three types of (unexpressed) restrictions on the ordering of subtasks in the specification are considered. For each type, a partial satisfaction method is introduced, which guarantees the generation of trajectories that do not violate the safety constraints while attending to partially satisfying the cosafety requirements with respect to the chosen restriction type. The efficacy of the framework is illustrated through case studies on a hybrid car-like robot in an office environment. [ABSTRACT FROM AUTHOR]

This paper investigates the motion of a free ball with initial velocity and angular velocity on a horizontal plane under friction. The ball will first slide along a parabolic trajectory (with its contact velocity in constant direction) and then roll along a straight trajectory. Curving of the sliding trajectory can be utilized for obstacle avoidance in path planning. A one-to-one correspondence exists between curved trajectories of the ball from one point to another and pairs of sliding and rolling directions that span a cone at the starting point to contain the ending point in the interior. This leads to a compact 2-D trajectory space, in which obstacles induce monotonic constraint curves. Based on the monotonicity, a plane sweep algorithm is described to find all collision-free trajectories. The work has several distinct features: handling of both sliding and rolling; planning in the configuration space of trajectories not that of the ball; computation of not just one collision-free trajectory but all such trajectories; and, finally, combining dynamics with computational geometry. The application of the work to spherical robots is discussed at the end. [ABSTRACT FROM AUTHOR]

In this paper, a service-oriented multiagent system (SoMAS) for the control and analysis of the cyber–physical system (CPS) in manufacturing automation utilizing a noncontact dynamic obstacle avoidance seven-DoF robot arm is presented. The interfaces of the services which the robot arm subsystem should provide to fully exploit its capability are identified. Specifically, the services of moving, object recognition, object fetching, and safety of human–robot interaction are considered as the fundamental functionalities that the robot arm should provide. The way to evaluate the quality of services (QoS) for the robot arm subsystem is also explained. To build such a robot arm subsystem, the system architecture is proposed. Also, implementation for the subsystem which includes: 3-D model-based object recognition, grasp database for object fetching, and online noncontact obstacle avoidance for the safety of human–robot interaction is provided. The experimental results demonstrate that the capability of 3-D model-based object recognition, object fetching, and dynamic collision avoidance are successfully implemented. [ABSTRACT FROM AUTHOR]

This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme. [ABSTRACT FROM PUBLISHER]

In this brief, a receding horizon control scheme for solving the obstacle avoidance motion planning problem of autonomous vehicles operating in uncertain dynamic environments is developed. By considering robots described by discrete-time linear time-invariant models, we propose a set-theoretic-based architecture to deal with disturbance effects, and input and state constraints. As one of its main merits, the strategy is capable of taking care of time-varying obstacle scenarios by jointly exploiting robust positively invariant region and one-step controllable set concepts. The resulting scheme guarantees uniformly ultimate boundedness and constraints fulfilment regardless of any admissible obstacle configuration. [ABSTRACT FROM PUBLISHER]

New sensing and steering technologies enable safety systems that work with the driver to ensure a safe and collision-free vehicle trajectory using a shared control approach. These shared control systems must constantly balance the sometimes competing objectives of following the driver’s command and maintaining a feasible trajectory for the vehicle. This paper presents a novel technique for creating haptic steering feedback based on a prediction of the system’s need to intervene in the future. This feedback mirrors the tension between the two controller objectives of following the driver and maintaining a feasible path. The paper uses simulation and experiment to investigate the impact of varying the prediction horizon on system performance. A novel in-vehicle driver study based on decoupling visual and haptic cues demonstrates that this feedback provides a statistically significant improvement in response time and reduced time to collision (TTC) in an obstacle avoidance task. [ABSTRACT FROM AUTHOR]

Fluid motion planners are a type of artificial potential field (APF) motion planners that use the differential equations of fluid flow to determine the desired trajectory. The fluid flow approach in motion planning can efficiently produce natural-looking trajectories. However, the differential equations used in previous studies are restricted to motion planning in 2-D environments. In this paper, the fluid flow approach is extended to a motion planning framework for 3-D mobile robots that avoids spheroidal obstacles. Compared with existing APF approaches, kinematic constraints in both speed and curvature are also considered. Possessing the efficiency of 2-D fluid motion planners, the proposed approach is able to plan natural-looking reference trajectories for nonholonomic 3-D mobile robots. The approach is demonstrated through various 3-D example scenarios. The work can be considered as a fundamental framework for 3-D fluid motion planning, where additional kinematic constraints and more complex scenarios can be incorporated. [ABSTRACT FROM PUBLISHER]

We present a novel design of an intelligent robotic hospital bed, named Flexbed, with autonomous navigation ability. The robotic bed is developed for fast and safe transportation of critical neurosurgery patients without changing beds. Flexbed is more efficient and safe during the transportation process comparing to the conventional hospital beds. Flexbed is able to avoid en-route obstacles with an efficient easy-to-implement collision avoidance strategy when an obstacle is nearby and to move towards its destination at maximum speed when there is no threat of collision. We present extensive simulation results of navigation of Flexbed in the crowded hospital corridor environments with moving obstacles. Moreover, results of experiments with Flexbed in the real world scenarios are also presented and discussed. [ABSTRACT FROM AUTHOR]

This paper proposes an obstacle avoidance method in the position stabilization of the wheeled mobile robots using interval type-2 fuzzy neural network (IT2FNN). Previously, we have proposed the unified strategies of obstacle avoidance and shooting method of the robot soccer system using type-1 fuzzy neural network (T1FNN). Even though the previous T1FNN method can achieve the required tasks, the performance of the previous T1FNN method is not satisfactory in the following sense. The previous T1FNN cannot reduce the influence of uncertainties effectively because it uses the crisp set as the membership values. In addition, it can result in the large oscillation behavior during the obstacle avoidance. Accordingly, we should design the IT2FNN method to improve the performance with smoother behavior as well as improved obstacle avoidance. The proposed IT2FNN method has the fuzzy neural network structure different from the T1FNN. Since the IT2FNN uses the fuzzy set instead of the crisp set as the membership values and it is robust against uncertainties, the performance of the robot behavior can be significantly improved especially in the presence of obstacles. Both simulation and experimental results using the actual wheeled mobile robot with the vision information are provided to show the validity and the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Persons with post-stroke visuospatial neglect (VSN) often collide with moving obstacles while walking. It is not well understood whether the collisions occur as a result of attentional- perceptual deficits caused by VSN or due to post-stroke locomotor deficits. We assessed individuals with VSN on a seated, joystick-driven obstacle avoidance task, thus eliminating the influence of locomotion. Twelve participants with VSN were tested on obstacle detection and obstacle avoidance tasks in a virtual environment that included three obstacles approaching head-on or 30 contralesionally/ipsilesionally. Our results indicate that in the detection task, the contralesional and head-on obstacles were detected at closer proximities compared to the ipsilesional obstacle. For the avoidance task collisions were observed only for the contralesional and head-on obstacle approaches. For the contralesional obstacle approach, participants initiated their avoidance strategies at smaller distances from the obstacle and maintained smaller minimum distances from the obstacles. The distance at detection showed a negative association with the distance at the onset of avoidance strategy for all three obstacle approaches. We conclusion the observation of collisions with contralesional and head-on obstacles, in the absence of locomotor burden, provides evidence that attentional-perceptual deficits due to VSN, independent of post-stroke locomotor deficits, alter obstacle avoidance abilities. [ABSTRACT FROM AUTHOR]

As VLSI technology continuously scales down, buffered clock tree synthesis (CTS) has become increasingly critical in an attempt to generate a high-performance synchronous chip design. This paper presents a novel obstacle-avoiding CTS approach with slew constraints satisfied and signal polarity corrected. We build a look-up table through NGSPICE simulation to achieve accurate buffer delay and slew, which guarantees that the final skew after NGSPICE simulation is as satisfactory as expected. Aiming at skew optimization under constraints of slew and obstacles, our CTS approach features the clock tree construction stage with the obstacle-aware topology generation algorithm called OBB, balanced insertion of candidate buffer positions and a fast heuristic buffer insertion algorithm. With an overall view on obstacles to explore the global optimization space, our CTS approach effectively overcomes the negative influence on skew brought by the obstacles. Experimental results show the effectiveness of our CTS approach with significantly improved skew and latency by 69.0% and 72.0% on average. In addition, the accuracy of the look-up table is demonstrated through the huge skew reduction by 87.3% on average. Moreover, our OBB heuristic algorithm obtains 53.2% improvement in skew than the classic balanced bipartition algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a new inequality-based criterion is proposed and investigated for obstacle avoidance of redundant robot manipulators at the joint-acceleration level. By incorporating such a dynamically updated inequality criterion and the joint physical constraints (i.e., joint-angle limits, joint-velocity limits, and joint-acceleration limits), a novel minimum-acceleration-norm (MAN) scheme is presented and investigated for robots' redundancy resolution. In addition, the resultant obstacle-avoidance MAN scheme resolved at the joint-acceleration level is reformulated as a general quadratic program (QP). Moreover, two important “Bridge” theorems are established, which show that such a QP problem can be equivalent to linear variational inequality (LVI) and then to piecewise-linear projection equation (PLPE). An LVI-based numerical method is thus developed and applied for online solution of the QP problem and the inequality-based obstacle-avoidance MAN scheme. Simulative results based on the PA10 robot manipulator in the presence of window-shaped and point obstacles further demonstrate the efficacy and superiority of the proposed acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators. In addition, experimental verification conducted on a practical six-link planar robot manipulator substantiates the effectiveness and physical realizability of the proposed obstacle-avoidance scheme. [ABSTRACT FROM PUBLISHER]

This paper deals with constrained image-based visual servoing of circular and conical spiral motion about an unknown object approximating a single image point feature. Effective visual control of such trajectories has many applications for small unmanned aerial vehicles, including surveillance and inspection, forced landing (homing), and collision avoidance. A spherical camera model is used to derive a novel visual-predictive controller (VPC) using stability-based design methods for general nonlinear model-predictive control. In particular, a quasi-infinite horizon visual-predictive control scheme is derived. A terminal region, which is used as a constraint in the controller structure, can be used to guide appropriate reference image features for spiral tracking with respect to nominal stability and feasibility. Robustness properties are also discussed with respect to parameter uncertainty and additive noise. A comparison with competing visual-predictive control schemes is made, and some experimental results using a small quad rotor platform are given. [ABSTRACT FROM PUBLISHER]

We present a planning algorithm for a mobile robot in unknown indoor dynamic environments. The algorithm takes into account the robot's own dynamics and the dynamic obstacles’ future behavior in a safety hierarchy. It also uses an appropriate time horizon for planning and respects the timing constraints on various modules of the planner that are imposed by interleaving of planning and execution. We provide a formulation of this safety hierarchy in a single step via a composite costmap. The planning algorithm has been implemented in Player/Stage. We have run extensive simulations and compared the performance of the full safety hierarchy with various permutations of safety levels, as well as with some baseline algorithms over three different performance measures. We show that our safety hierarchy performs statistically significantly better across most performance measures and most simulation scenarios. [ABSTRACT FROM PUBLISHER]

Effective and efficient motion of humanoid robots in unstructured dynamic environments is a prerequisite for their activity in the living and working environment of humans. Motion in such environments has to be adjusted all the time to suit the current conditions. This paper presents a method for the synthesis and realization of the biped robot motion (walking) composed of simple movements—primitives, because any complex motion can be composed of tied primitives. The primitives are parametrized with the relationship established between the overall motion characteristics and their own parameters. This way, it is possible to achieve online modification at any moment. The proposed solution was tested by the simulation involving a dynamic robot model. The results demonstrate that it is possible to generate a dynamically balanced walk that can be modified online at any moment of its realization. [ABSTRACT FROM PUBLISHER]

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET planner deliberately trades probabilistic completeness for computational efficiency. This tradeoff enables the EET planner to outperform state-of-the-art sampling-based planners by up to three orders of magnitude. We show that these considerable speedups apply for a variety of challenging real-world motion planning problems. The performance improvements are achieved by leveraging work space information to continuously adjust the sampling behavior of the planner. When the available information captures the planning problem's inherent structure, the planner's sampler becomes increasingly exploitative. When the available information is less accurate, the planner automatically compensates by increasing local configuration space exploration. We show that active balancing of exploration and exploitation based on workspace information can be a key ingredient to enabling highly efficient motion planning in practical scenarios. [ABSTRACT FROM PUBLISHER]

Conventional robotic vacuum cleaners (RVCs) with ultrasonic or infrared (IR) sensors present problems in detecting obstacles when they clean the floor in complex situations, for example, under tables or chairs with thin legs. This paper presents a robust obstacle detection (OD) method based on the triangulation principle for RVCs operating in various home environments. The proposed method uses the IR emitter of the RVC to project a horizontal IR beam toward the floor, following which the RVC?s wide-angle vision camera captures an image that includes the IR line reflected by the floor or an obstacle. Obstacles are detected by using the image coordinates of the pixels that belong to the IR line in the captured image. Accurate separation of the IR line from the image background is accomplished by defining and minimizing an energy function based on the characteristics of the IR line. The proposed method was tested on the embedded RVC system and was shown capable of achieving OD performance compared with existing methods. [ABSTRACT FROM PUBLISHER]

This paper presents an information potential method for integrated path planning and control. The method is applicable to unicycle robotic sensors deployed to classify multiple targets in an obstacle-populated environment. A new navigation function, referred to as information potential, is generated from the target conditional mutual information, and used to design a closed-loop stable switched control law. The information potential is shown to obey the properties of potential navigation functions and to enable measurements that maximize the information value over time. The information potential is also used to construct a local roadmap for escaping local minima. The properties and computational complexity of the local roadmap algorithm are analyzed. Numerical simulation results show that the method outperforms other strategies, such as rapidly exploring random trees and classical potential field methods. [ABSTRACT FROM PUBLISHER]