Avoidance Problem Research Articles

Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight

Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.

Double Broad Reinforcement Learning Based on Hindsight Experience Replay for Collision Avoidance of Unmanned Surface Vehicles

Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a large area of sea. To overcome the dilemma, we propose a double broad reinforcement learning based on hindsight experience replay (DBRL-HER) for the collision avoidance system of USVs to improve the efficiency and accuracy of decision-making. The algorithm decouples the two steps of target action selection and target Q value calculation to form the double broad reinforcement learning method and then adopts hindsight experience replay to allow the agent to learn from the experience of failure in order to greatly improve the sample utilization efficiency. Through training in a grid environment, the collision avoidance success rate of the proposed algorithm was found to be 31.9 percentage points higher than that in the deep Q network (DQN) and 24.4 percentage points higher than that in BRL. A Unity 3D simulation platform with high fidelity was also designed to simulate the movement of USVs. An experiment on the platform fully verified the effectiveness of the proposed algorithm.

Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach

Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex issue that is faced by UAV is obstacle / object avoidance. Obstacle detection and avoidance are important for UAV and it is the complex problem to solve due to the payload restriction. This will limit the sensor count mounted on the vehicle. Radar was used to find the distance between the object and vehicle. This can help to detect and track the moving objects speed and direction towards the vehicle. This paper considered the object avoidance problem as path planning problem. There were many path planning methods related to UAV which formulates the path planning as an optimization problem to avoid the obstacles. With the consideration, this paper proposed an efficient and optimal approach called Floyd Warshall- Differential evolution (FWDE) approach to detect the frontal obstacles of UAV. Finally, statistical analysis of the simulated environment reveals that the proposed evolutionary method can efficiently avoid both static and dynamic objects for UAVs. This efficient avoidance algorithm for UAV can be experimented with simulation environment with three kinds of scenarios having different number of cells. The obtained accuracy and recall value of the proposed system is 95.21% and 91.56%.

A dense obstacle avoidance algorithm for UAVs based on safe flight corridor

Aiming at the problem of autonomous obstacle avoidance of fixed-wing UAVs in a complex, dense and multi-obstacle environment, a path planning algorithm for fixed-wing UAVs based on a safe flight corridor is proposed. The difficulty of avoiding dense obstacles lies in the choice of obstacle circumvention and traversal: although circumvention is safer, the flight cost is greater; although the traversal cost is lower, the safety threat is higher. How to quickly solve the optimal path is the core issue. This paper firstly defines a safe flight corridor innovatively based on the maneuvering characteristics of fixed-wing UAVs and the Dubins curves. By comprehensively considering UAV flight safety and flight costs, an obstacle threat evaluation function is constructed. Secondly, in view of the computational complexity caused by the dense obstacles, an obstacle clustering algorithm based on obstacle density is proposed, and the nonlinear evaluation function in a high dynamic environment is quickly approximated by Monte Carlo sampling method. Finally, simulations verify the effectiveness of the proposed algorithm in solving dense obstacle avoidance for fixed-wing UAVs.

Recording of patients’ mental health and quality of life-related outcomes in primary care: a cross-sectional study in the UK

ObjectiveTo compare patient-reported anxiety, depression and quality-of-life (QoL) outcomes, with data registered in patients’ primary care electronic health record (EHR).DesignCross-sectional study.SettingPrimary care in the UK.ParticipantsA convenience sample of 608 women...

Convex Optimization for Parameter Synthesis in MDPs

Probabilistic model-checking aims to prove whether a Markov decision process (MDP) satisfies a temporal logic specification. The underlying methods rely on an often unrealistic assumption that the MDP is precisely known. Consequently, parametric MDPs (pMDPs) extend MDPs with transition probabilities that are functions over unspecified parameters. The parameter synthesis problem is to compute an instantiation of these unspecified parameters such that the resulting MDP satisfies the temporal logic specification. We formulate the parameter synthesis problem as a quadratically constrained quadratic program, which is nonconvex and is NP-hard to solve in general. We develop two approaches that iteratively obtain locally optimal solutions. The first approach exploits the so-called convex–concave procedure (CCP), and the second approach utilizes a sequential convex programming (SCP) method. The techniques improve the runtime and scalability by multiple orders of magnitude compared to black-box CCP and SCP by merging ideas from convex optimization and probabilistic model-checking. We demonstrate the approaches on a satellite collision avoidance problem with hundreds of thousands of states and tens of thousands of parameters and their scalability on a wide range of commonly used benchmarks.

Addiction to New Technologies and Cyberbullying in the Costa Rican Context

Addiction to new technologies (Internet, smartphone and video games) as well as cyber-aggression presents a growing incidence at a global level. Correlational research is proposed with a sample of n = 127 Costa Rican adolescents, with a mean age of 16.32 years, comprising 67 men and 60 women. The objective was to determine the prevalence of Internet, smartphone and video game addiction and its close relationship with cyberbullying (cyber-victim role, cyberbully role and mixed role) in the Costa Rican context. The results show a high rate of abusive use (connection time) of the Internet and “smartphones”, and the correlation study, multiple regressions, backward elimination method and network analysis show how the behavior of cyberbullies is moderately mediated by “Lack of control of Smartphone” and to a large extent by “Avoidance and social problem video games”, while that of victims is moderately mediated by “Avoidance and social problem video games”, and is slightly mediated for “Lack of control of Smartphone” and for “Lack of control of Internet”. In addition, there is a strong relationship between holding both the role of cyber-victim and cyberbully, developing feelings of revenge and lack of empathy. The practical implications, and the relevance of the socio-demographic and social explanatory variables of both phenomena are discussed. It is concluded that there is evidence of an explanatory and close relationship between the phenomena of cybervictimization, cyber-aggression and addiction to the Internet, smartphone and video games.

Study on USV Cluster Formation Control and Cooperative Obstacle Avoidance

With the deepening of research on unmanned vehicles, research on USV (Unmanned Surface Vehicle) has gradually become extensive. In order to cope with complex Marine operations, the study of USV cluster collaborative operations has been emphasized. Therefore, this paper explores USV cluster formation and collaborative obstacle avoidance. In this paper, a formation control method of USV cluster is proposed based on the leader-follower architecture and the consistency algorithm. To solve the problem of formation cooperative obstacle avoidance, the traditional APF (APF method) is improved to avoid falling into the local optimal, and the cluster cooperative obstacle avoidance method is proposed. Finally, the simulation results show that the USV cluster formation control and obstacle avoidance methods are effective.

Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles.

Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs' collision avoidance model.

UAVs Maneuver Decision-Making Method Based on Transfer Reinforcement Learning.

Aiming at the 1vs1 confrontation problem in a complex environment where obstacles are randomly distributed, the DDPG (deep deterministic policy gradient) algorithm is used to design the maneuver decision-making method of UAVs. Traditional methods generally assume that all obstacles are known globally. In this paper, a UAV airborne lidar detection model is designed, which can effectively solve the problem of obstacle avoidance when facing a large number of unknown obstacles. On the basis of the designed model, the idea of transfer learning is used to transfer the strategy trained by one UAV in a simple task to a new similar task, and the strategy will be used to train the strategy of the other UAV. This method can improve the intelligence of the UAVs in both sides alternately and progressively. The simulation results show that the transfer learning method can speed up the training process and improve the training effect.

Control optimisation of automated guided vehicles in container terminal based on Petri network and dynamic path planning

• The task optimization and path planning of vehicles considering dynamic obstacle avoidance remain to be the urgent problems to be solved, so as to realize intelligence of container handling in container terminals. • The scheduling framework model of automated guided vehicle is established based on heuristic rules and OOTCSPN with vehicle behavior pattern as the top-level object, which can direct the traffic order and position sequence of the automated guided vehicle fleet. • Based on A* fusion DWA, path planning algorithm of automated guided vehicles is designed. Experiments show that the improved algorithm can improve vehicle operation efficiency, plan the route more smoothly and achieve random obstacle avoidance. To solve the problem of path optimisation and obstacle avoidance of automated guided vehicles (AGV) in the horizontal transportation of container terminals, this study proposed three operation modes of terminal vehicles and summarised four vehicle behaviour modes. Considering the vehicle behaviour pattern as the top-level object, based on heuristic rules and an object-oriented timed coloured stochastic Petri network (OOTCSPN) algorithm, a scheduling framework model of the AGV was established to optimise vehicle scheduling. A path planning model for three vehicle operation modes was designed for AGV path planning and real-time random obstacle avoidance based on the A-star(A*) algorithm fusion dynamic window approach (DWA). The simulation results indicate that the container handling efficiency increased by 13.2% using the improved algorithm. The scaled-down experiment confirms that the proposed method makes the planned path smoother and can also achieve real-time obstacle avoidance.

Adults with Animal Phobia: Systematic Replication of Clinical Cases

AbstractAnimal phobia is common in children, but it is also frequent in adults who maintain the problem throughout their lives. Eleven cases of animal phobia that were treated with progressive multimedia exposure are presented. The participants were two men and nine women (aged 19–27 years), with anxiety and avoidance problems in relation to various animals. A single-case A-B-Follow-up design was carried out, with concurrent control of several baselines; and a systematic between-subjects replication, with the same treatment repeated across different participants and types of phobias. A behavioural interview, phobia questionnaires, and Multimedia Behavioural Avoidance Test (M-BAT) with pictures and videos of animals, as well as heart rate, were used for assessment purposes. The intervention was the progressive multimedia exposure in four phases (photographs, videos, simulated animals, and live exposure), in addition to diaphragmatic breathing, and homework assignments. The results replicate the same efficacy in each of the participants, with statistically significant and clinical changes in their daily lives. Also, the data as a group show this success with a high Cohen's d effect size (between −1.63 and −30.03). We conclude with an appraisal of the usefulness of the procedure for adults with phobias who do not tolerate direct exposure.

A two-level optimization algorithm for path planning of bionic robotic fish in the three-dimensional environment with ocean currents and moving obstacles

Path planning is not only the premise of underwater navigation and obstacle avoidance, but also the core and key to improve the operation durability for the bionic robotic fish. In this paper, the path length, smoothness and safety factor are selected as the optimization performance indexes. In order to solve the path planning with ocean currents and moving obstacles, a two-level optimization algorithm is proposed for the bionic robotic fish. In the first level of optimization, it solves the global path planning problem with known environment. In the second level of optimization, based on the optimization results of the first level of optimization, the local optimal path is obtained by the dynamic optimization method in the environment with unknown static and moving obstacles, which can solve the problem of obstacle avoidance for irregular obstacles effectively. The two-level optimization algorithm improves the accuracy of the path planning. Fuzzy adaptive genetic algorithm (FAGA) is given to further improve the optimization performance. The proposed algorithm can improve the optimization performance of the path planning, whose effectiveness is demonstrated by the simulation and experiment results.

Collision avoidance for second-order mobile robots

This paper addresses the collision avoidance problem for a group of two second-order mobile robots. The proposed control strategy is composed of a part that achieves a desired formation, based on the Fundamental Consensus Algorithm, which ensures that agents converge to the desired formation. On the other hand, Artificial Vector Fields (AVF) are used for collision avoidance. Furthermore, a parameter is designed to allow an adequate scaling of the AVF and to ensure non-collision. Numerical simulations are presented to validate the performance of the proposed control strategy.

Ship Collaborative Path Planning Method Based on CS-STHA

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

COLREGs-Compliant Multi-Ship Collision Avoidance Based on Multi-Agent Reinforcement Learning Technique

The congestion of waterways can easily lead to traffic hazards. Moreover, according to the data, the majority of sea collisions are caused by human error and the failure to comply with the Convention on the International Regulation for the preventing Collision at Sea (COLREGs). To avoid this situation, ship automatic collision avoidance has become one of the most important research issues in the field of marine engineering. In this study, an efficient method is proposed to solve multi-ship collision avoidance problems based on the multi-agent reinforcement learning (MARL) algorithm. Firstly, the COLREGs and ship maneuverability are considered for achieving multi-ship collision avoidance. Subsequently, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is utilized to detect and reduce the risk of collision. Ships can operate at the safe velocity computed by the ORCA algorithm to avoid collisions. Finally, the Nomoto three-degrees-of-freedom (3-DOF) model is used to simulate the maneuvers of ships. According to the above information and algorithms, this study designs and improves the state space, action space and reward function. For validating the effectiveness of the method, this study designs various simulation scenarios with thorough performance evaluations. The simulation results indicate that the proposed method is flexible and scalable in solving multi-ship collision avoidance, complying with COLREGs in various scenarios.

Study on UAV obstacle avoidance algorithm based on deep recurrent double Q network

The traditional reinforcement learning method has the problems of overestimation of value function and partial observability in the field of machine motion planning, especially in the obstacle avoidance problem of UAV, which lead to long training time and difficult convergence in the process of network training. This paper proposes an obstacle avoidance algorithm for UAVs based on a deep recurrent double Q network. By transforming the single-network structure into a dual-network structure, the optimal action selection and action value estimation are decoupled to reduce the overestimation of the value function. The fully connected layer introduces the GRU recurrent neural network module, and uses the GRU to process the time dimension information, enhance the analyzability of the real neural network, and improve the performance of the algorithm in some observable environments. On this basis, combining with the priority experience playback mechanism, the network convergence is accelerated. Finally, the original algorithm and the improved algorithm are tested in the simulation environment. The experimental results show that the algorithm has better performance in terms of training time, obstacle avoidance success rate and robustness.

Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds

This paper presents a closed-form approach to constrain a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. We show that the obstacle avoidance problem can be inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume and obstacles, and will asymptotically converge towards an attractor. We further create smooth motion fields around obstacles with edges (e.g. tables). Both obstacles and enclosures may be time-varying, i.e. moving, expanding and shrinking. The technique enables a robot to navigate within an enclosed corridor while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment, and also tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in presence of a diverse crowd with a non-uniform motion pattern.

Survey of Collision Avoidance Systems for Underground Mines: Sensing Protocols.

With the growing number of unintentional interactions occurring in underground mines, Collision Avoidance System (CAS) establishment and maintenance has become an urgent need for mining industries to enhance their risk profile and improve construction safety. Usually, most collision accidents can be divided into three different categories in line with the involved participants and infrastructure condition. The accidents pose a great risk of financial cost to mining companies and even cause casualties. In detail, this paper presents an intensive study survey of positioning techniques, including ranging algorithms, to accommodate the demands of various proximity sensors and improve the capability of situational awareness. Then, we exploit the importance of the communication system, prevalent low-power wide-area technologies and related communication protocols. The effectiveness of communication systems decides and facilitates the success of the final integrated system that can be used to fundamentally address the problem of collision avoidance. For the purpose of collaboration between communication systems and other executive departments, a series of systematic comparisons of pertinent technologies and algorithms is given near the end, followed by a brief discussion on the best choice among these options. In the proposed solution, the overall end-to-end delay can be minimised to a few nanoseconds and the localisation accuracy can achieve centimetre level when operating in the range of 100 m.

Optimization-based distributed safety control with applications to collision avoidance for mobile robotic networks

This paper presents a distributed input rectification methodology to guarantee the safety of a multi-agent system over its pre-given cooperative control task. We start with defining a multi-agent system as a control target, which consists of a group of agents with general control affine nonlinear dynamics and an interconnection topology between agents. The research objective, safety, is next defined via a set invariance property, where a safe set is formed by control barrier functions. Then, an optimization-based distributed safety control method is presented. In the proposed approach, each agent rectifies its control input by solving an individual optimization problem to achieve a pre-given cooperative control strategy as much as possible while satisfying constraints for the safety derived by investigating the control barrier functions. Here, the optimization problem is formulated in a distributed manner by dividing the safety constraint between agents. To show the utility of the present approach, collision avoidance problems of a mobile robotic network are investigated as case studies.