Abstract: A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

Abstract: In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

Abstract: This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety. First, we develop an indoor map editor to parse geometric information from architectural models and generate a semantic map consisting of a global 2D traversable grid map layer and context-aware layers. By leveraging the visual positioning service (VPS) within the Google Tango device, we design a map alignment algorithm to bridge the visual area description file (ADF) and semantic map to achieve semantic localization. Using the on-board RGB-D camera, we develop an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm. A multi-modal human-machine interface (HMI) is designed with speech-audio interaction and robust haptic interaction through an electronic SmartCane. Finally, field experiments by blindfolded and blind subjects demonstrate that the proposed system provides an effective tool to help blind individuals with indoor navigation and wayfinding.

Abstract: An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research.

Abstract: Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.

Abstract: Navigation robots have the potential to overcome some of the limitations of traditional navigation aids for blind people, specially in unfamiliar environments. In this paper, we present the design of CaBot (Carry-on roBot), an autonomous suitcase-shaped navigation robot that is able to guide blind users to a destination while avoiding obstacles on their path. We conducted a user study where ten blind users evaluated specific functionalities of CaBot, such as a vibro-tactile handle to convey directional feedback; experimented to find their comfortable walking speed; and performed navigation tasks to provide feedback about their overall experience. We found that CaBot's performance highly exceeded users' expectations, who often compared it to navigating with a guide dog or sighted guide. Users' high confidence, sense of safety, and trust on CaBot poses autonomous navigation robots as a promising solution to increase the mobility and independence of blind people, in particular in unfamiliar environments.

Abstract: This paper addresses the driver–automation shared driving control for lane keeping and obstacle avoidance of automated vehicles in highway traffic. The proposed shared control framework is established from a novel cooperative trajectory planning algorithm and a fuzzy steering controller. Based on polynomial functions, the cooperative trajectory planning is formulated by judiciously exploiting the information on the maneuver decision, the conflict management, and the driver monitoring. As a result, the planned trajectory of the vehicle is continuously adapted according to the driver's actions and intentions. By means of Lyapunov stability arguments, sufficient conditions in terms of linear matrix inequalities are given to design a Takagi–Sugeno fuzzy model-based controller. This robust steering controller provides a necessary assistive torque to track the planned vehicle trajectory. The new shared driving control framework allows reducing effectively the driver–automation conflict issue while offering the driver more freedom to swerve within a predefined lane. The advantages of the proposed approach are evaluated using both objective and subjective results, experimentally obtained from several human drivers and an advanced interactive dynamic driving simulator.

Abstract: 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: Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

Abstract: This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other.Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly.Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6D camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance.

Abstract: Trajectory planning and tracking control are two keys of collision avoidance for autonomous vehicles in critical traffic scenarios. It requires not only the system functionality, but also strong real-time. In this paper, we integrated trajectory planner and tracking controller for autonomous vehicle to implement trace planning and tracking for obstacle avoidance. The trajectory planner is based on the state lattice approach and the tracking controller is designed based on the model predictive control using the vehicle kinematics model. The simulation shows that the planner can generate smooth trajectories which could be selected as references for the controller. The maximum tracking error is less than 0.2 m when the vehicle speed is below 50 km/h. Additionally, the on field test shows that the test vehicle with this method is capable of following the reference path accurately, even at sharp corners.

Abstract: This paper presents an implementation of autonomous mobile robot with the robot operating system (ROS). The system utilizes 2D LiDAR and RGB-D camera with ROS 2D navigation stack, with low power consumption and inexpensive onboard computer. Safe to property and human is of priority. Regarding software, we use official ROS packages with minimal default parameter changes. For hardware, the limitation of equipment and system setting are among challenges. Our proposed systems can perform navigation with dynamic obstacle avoidance capability. The Contribution of this paper is two system setups of ROS navigation stack are proposed. The first system is implemented on Raspberry Pi 3 using 2D LiDAR only. The second system is implemented on Intel NUC using 2D LiDAR and RGB-D camera. To evaluate the performance, usability testing was performed in multiple experiments. Our experiment results show that the robot can avoid objects in their path, or stop in case of unavoidable. Discussion of problems and solutions are presented after the experiment results.

Abstract: Human–robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a task in a not fully repetitive manner using their expertise and cognitive capabilities. The traditional robot programming cannot cope with these challenges of human–robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. Through the demonstrations, the robot learns the sequence of actions for an assembly task (high-level learning) without the need of pre-programming. Additionally, the robot learns every path as needed for object manipulation (low-level learning). Once the robot has the knowledge of the demonstrated task, it can perform the task in collaboration with the human. However, the need for adaptation of the learned knowledge may arise as the human collaborator could introduce changes in the environment, such as placing an object to be manipulated in a position and orientation different from the demonstrated ones. In this paper, a novel real-time adaptation algorithm to cope with these changes in the environment, introduced by the human factor, is proposed. The proposed algorithm is able to identify the sequence of actions needed to be performed in a new environment. A Gaussian Mixture Model-based modification algorithm is able to adapt the learned path in order to enable robot to successfully complete the task without the need of additional training by demonstration. The proposed framework copes with changes in the position and orientation of the objects to be manipulated and also provides obstacle avoidance. Moreover, the framework enables the human collaborator to suggest different sequence of actions for the learned task, which will be performed by the robot. The proposed algorithm was tested on a dual-arm industrial robot in an assembly scenario and the results are presented. Shown results demonstrate a potential of the proposed robot learning framework to enable continuous human–robot collaboration.

Abstract: Assistive devices for visually impaired people (VIP) which support daily traveling and improve social inclusion are developing fast. Most of them try to solve the problem of navigation or obstacle avoidance, and other works focus on helping VIP to recognize their surrounding objects. However, very few of them couple both capabilities (i.e., navigation and recognition). Aiming at the above needs, this paper presents a wearable assistive device that allows VIP to (i) navigate safely and quickly in unfamiliar environment, and (ii) to recognize the objects in both indoor and outdoor environments. The device consists of a consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU), which are mounted on a pair of eyeglasses, and a smartphone. The device leverages the ground height continuity among adjacent image frames to segment the ground accurately and rapidly, and then search the moving direction according to the ground. A lightweight Convolutional Neural Network (CNN)-based object recognition system is developed and deployed on the smartphone to increase the perception ability of VIP and promote the navigation system. It can provide the semantic information of surroundings, such as the categories, locations, and orientations of objects. Human–machine interaction is performed through audio module (a beeping sound for obstacle alert, speech recognition for understanding the user commands, and speech synthesis for expressing semantic information of surroundings). We evaluated the performance of the proposed system through many experiments conducted in both indoor and outdoor scenarios, demonstrating the efficiency and safety of the proposed assistive system.

Abstract: In this paper, we present our recent advances in both theoretical methods and field experiments for the coordinated control of miniature fixed-wing unmanned aerial vehicle (UAV) swarms. We propose a multi-layered group-based architecture, which is modularized, mission-oriented, and can implement large-scale swarms. To accomplish the desired coordinated formation flight, we present a novel distributed coordinated-control scheme comprising a consensus-based circling rendezvous, a coordinated path-following control for the leader UAVs, and a leader-follower coordinated control for the follower UAVs. The current framework embeds a formation pattern reconfiguration technique. Moreover, we discuss two security solutions (inter-UAV collision avoidance and obstacle avoidance) in the swarm flight problem. The effectiveness of the proposed coordinated control methods was demonstrated in field experiments by deploying up to 21 fixed-wing UAVs.

Abstract: Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed. Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement learning method in the indoor navigation context. We evaluate the method in simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on differential-drive robots at three physical sites. Our results show PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on robots, including over 5.8 kilometers of physical robot navigation. Video: this https URL

Abstract: This letter presents a closed-form approach to obstacle avoidance for multiple moving convex and star-shaped concave obstacles. The method takes inspiration in harmonic-potential fields. It inherits the convergence properties of harmonic potentials. We prove impenetrability of the obstacles hull and asymptotic stability at a final goal location, using contraction theory. We validate the approach in a simulated co-worker industrial environment, with one KUKA arm engaged in a pick and place grocery task, avoiding in real-time humans moving in its vicinity and in simulation to drive wheel-chair robot in the presence of moving obstacles.

Abstract: Conditionally automated driving (SAE Level 3) relinquishes driver from monitoring the driving task and the traffic environment, permitting the driver to perform non-driving-related tasks (NDRT). Nevertheless, the driver must be available as a backup option. With this in mind, the current study aims at investigating the effect of the type of NDRT and takeover situations on driver performance. The NDRTs used were writing emails and watching videos, while the takeover situations tested were avoiding an obstacle on one’s lane and missing lane markings. Forty-four participants took part in a study carried in a dynamic simulator at PSA Peugeot Citroen Technical Center. Results showed that an effect of takeover situation on takeover time, with shorter times associated with obstacle avoidance, regardless of the type of NDRT. Measures of driver performance in the obstacle avoidance situation did not differ among manual and automated driving conditions, except for minimum time to collision. In the missing lane conditions, an effect of driving mode was observed on lateral and longitudinal control, as well as minimum time headway, regardless of the type of NDRT was observed. Our results suggest that the criticality of the situation had a distinct effect on takeover time and mental workload, while NDRT did not have a clear role. Furthermore, in line with previous research, drives’ need for control stabilization beyond takeover was documented.

Abstract: Modeling error or external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over feedback policies but at the expense of increased computational complexity. Tube MPC is an approximate solution strategy in which a robust controller, designed offline, keeps the system in an invariant tube around a desired nominal trajectory, generated online. Naturally, this decomposition is suboptimal, especially for systems with changing objectives or operating conditions. In addition, many tube MPC approaches are unable to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for nonlinear systems where both the tube geometry and open-loop trajectory are optimized simultaneously. By using boundary layer sliding control, the tube geometry can be expressed as a simple relation between control parameters and uncertainty bound; enabling the tube geometry dynamics to be added to the nominal MPC optimization with minimal increase in computational complexity. In addition, DTMPC is able to leverage state-dependent uncertainty to reduce conservativeness and improve optimization feasibility. DTMPC is demonstrated to robustly perform obstacle avoidance and modify the tube geometry in response to obstacle proximity.

Abstract: In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.

Abstract: This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protege language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance.

Abstract: Deliberative capabilities are essential for intelligent aerial robotic applications in modern life such as package delivery and surveillance. This paper presents a real-time 3D path planning solution for multirotor aerial robots to obtain a feasible, optimal and collision-free path in complex dynamic environments. High-level geometric primitives are employed to compactly represent the situation, which includes self-situation of the robot and situation of the obstacles in the environment. A probabilistic graph is utilized to sample the admissible space without taking into account the existing obstacles. Whenever a planning query is received, the generated probabilistic graph is then explored by an A⋆ discrete search algorithm with an artificial field map as cost function in order to obtain a raw optimal collision-free path, which is subsequently shortened. Realistic simulations in V-REP simulator have been created to validate the proposed path planning solution, integrating it into a fully autonomous multirotor aerial robotic system.

Abstract: The unmanned surface vehicle (USV) is usually required to perform some tasks with the help of static and dynamic environmental information obtained from different detective systems such as shipborne radar, electronic chart, and AIS system. The essential requirement for USV is safe when suffered an emergency during the task. However, it has been proved to be difficult as maritime traffic is becoming more and more complex. Consequently, path planning and collision avoidance of USV has become a hot research topic in recent year. This paper focuses on dynamic obstacle avoidance and path planning problem of USV based on the Ant Colony Algorithm (ACA) and the Clustering Algorithm (CA) to construct an auto-obstacle avoidance method which is suitable for the complicated maritime environment. In the improved ant colony-clustering algorithm proposed here, a suitable searching range is chosen automatically by using the clustering algorithm matched to different environmental complexities, which can make full use of the limited computing resources of the USV and improve the path planning performances firstly. Second, the dynamic searching path is regulated and smoothed by the maneuvering rules of USV and the smoothing mechanism respectively, which can effectively reduce the path length and the cumulative turning angle. Finally, a simulation example is provided to show that our proposed algorithm can find suitable searching range according to different obstacle distributions, as well as accomplish path planning with good self-adaptability. Therefore, a safe dynamic global path with better optimize performances is achieved with the help of multi-source information.

Abstract: Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

Abstract: The evolution of the target system (leader) in pinning-controlled complex networks may need to be regulated by some control inputs for performing various practical tasks, e.g., obstacle avoidance, tracking highly maneuverable target, and so on. Motivated by this observation, we shall investigate the global pinning synchronization problems for complex switching networks for which the target system is subject to nonzero control inputs. First, using the idea of unit vector function method, a discontinuous coupling law is designed. With the aid of stability theory for switched systems, it is theoretically shown that synchronization in the network under this discontinuous coupling law can be achieved by choosing sufficiently large coupling strengths if the average dwell time (ADT) is bounded below by a positive constant. Second, we use the boundary layer method to design a continuous-coupling law. It has been theoretically shown that the synchronization error is ultimately uniformly bounded (UUB) under this continuous-coupling law. The chattering effect is also avoided in real implementation by using this continuous-coupling law. Furthermore, for networks with unknown external disturbances and unmodeled dynamics, neuro-adaptive-based coupling laws are designed to ensure that the synchronization error of the networks with undirected switching communication topologies under these laws is UUB. The obtained theoretical results are finally validated by performing numerical simulation on coupling Chua’s circuit systems.