Collision Avoidance Performance Research Articles

AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning

In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-of-sight guidance method to mitigate abrupt changes in the yaw direction and smooth the heading changes when the AUV switches trajectory. The different experimental results show that the double deep Q-network algorithms ensure excellent collision avoidance performance. The effectiveness of the algorithm proposed in this paper was verified in three environments: random static, mixed static, and complex dynamic. The results show that the proposed algorithm has significant advantages over other algorithms in terms of success rate, collision avoidance performance, and generalization ability. The double deep Q-network algorithm proposed in this paper is superior to the genetic algorithm and deep learning in terms of the running time, total path, performance in avoiding collisions with moving obstacles, and planning time for each step. After the algorithm is trained in a simulated environment, it can still perform online learning according to the information of the environment after deployment and adjust the weight of the network in real-time. These results demonstrate that the proposed approach has significant potential for practical applications.

Study on route selection and collision avoidance of UUV based on fuzzy control

The route selection and collision avoidance performance of UUV was studied based on fuzzy control theory. Firstly, a six-degree-of-freedom motion experiment is simulated for the submarine, and a fuzzy controller is designed according to the motion parameters obtained from the experiment. The fuzzy control system includes the heading control and depth control, and uses the fuzzy BK triangle sub product algorithm to design the next instantaneous safety course, so as to achieve the anti-collision function. In the numerical simulation, the performance of the controller is evaluated by setting a single point obstacle and a multi-point obstacle, respectively, for both the heading control and the collision avoidance path planning. The results show that: The fuzzy control system has better function of route selection, can determine the best course in time, and can pass the obstacle on the course with enough safe distance, which verifies the feasibility of this method.

Study on spacecraft formation capture control method based on disturbance observer

In the presence of compound disturbances, a multi-spacecraft cooperative collision avoidance capture control method based on disturbance observer was proposed, which can solve the problem of low speed rolling non-cooperative target close-range capture in space. Firstly, a relative motion model of attitude and orbit coupling is established. Secondly, the disturbance observer is used to estimate and cancel the compound disturbance in the capture process. At the same time, the hyperquadric surfaces are used to describe the shape of space non-cooperative targets and capture spacecraft to establish a composite artificial potential field, and a robust control law with collision avoidance function is also designed. Finally, the stability of the controlled system is proved by using Lyapunov function, and the collision avoidance performance of the system is analyzed. Numerical simulations are carried out to evaluate the effectiveness of the proposed control scheme.

Optimal speed planning and collision avoidance control for electric vehicles considering traffic lights

<p indent=0mm>To reduce the energy consumption and avoid the risk of rear-end collisions for intelligent electric vehicles (EVs) during cruising under urban conditions, a hierarchical control system for energy saving and collision avoidance for EVs considering the information of traffic lights is proposed. The upper layer of the system plans the vehicle speed based on the aforementioned traffic information and a vehicle dynamic model and obtains the reference speed sequence with the minimum energy consumption through online rolling optimization; the bottom layer calculates the optimal acceleration of the vehicle in real-time along with the reference speed sequence and multiobjective function and performs collision avoidance control. Finally, the simulation model of the control system was built based on the Matlab/Simulink platform, and the effectiveness of the proposed control system was verified based on conditions of the presence and absence of human-driven vehicles in front of the EV. The upper layer can realize the optimal reference speed in vehicle speed planning according to the change in road traffic information and improve the energy consumption characteristics of the EV; the collision avoidance control by the lower layer can calculate the optimal acceleration control input by combining the optimal reference speed and the multiobjective function evaluation results. Thus, it is clear that the designed system can ensure energy-efficient, safe, and stable driving of EVs.

Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

Before highly automated vehicles (HAVs) become part of everyday traffic, their safety has to be proven. The use of human performance as a benchmark represents a promising approach, but appropriate methods to quantify and compare human and HAV performance are rare. By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of (human) performance is developed. The method is applied to a driving simulator study, in which participants are repeatedly confronted with a cut-in manoeuvre on a highway. By systematically manipulating the criticality of the manoeuvre in terms of time to collision, humans’ collision avoidance performance is measured. The limit of human performance is then identified by means of logistic regression. The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance. Accordingly, the presented approach represents one means by which HAVs’ safety performance could be proven.

Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels

In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances.

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty

We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraints: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. On average, the Gaussian method requires ~ 5 ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 9 ms on average in scenarios with 4 agents.

Connected vehicle driving safety enhancement via dynamic communication channel selection

Vehicle-to-Vehicle communication can cogently improve traffic safety because it grants drivers better situational awareness and strengthens inter-cooperation among them. However, the current policy obliges the safety-critical messages, i.e., Basic Safety Messages (BSMs) and Event Safety Messages (ESMs), to be transmitted exclusively in one single channel over the Dedicated Short-Range Communication (DSRC) spectrum, which may incur severe channel congestions, intolerable communication delays, and higher accident probabilities. To alleviate the channel congestion, popular measures focus on adaptively adjusting the transmission parameters, e.g., packet generating rate. However, due to the narrow bandwidth of a single channel, these methods can hardly ensure timely delivery of critical safety-related messages when the density of DSRC-enabled vehicles becomes high. Instead of sticking to only one single channel, this paper applies a dynamic channel selection algorithm to thoroughly exploit the entire DSRC band resource and reduce the transmission delay. To demonstrate the effectiveness of the utilized algorithm, we conduct experiments under two representative scenarios, i.e., a cooperative adaptive cruise control scenario and a run-the-red-light scenario at an intersection. Experimental results show that the transmission delays of both the BSMs and the ESMs could be effectively reduced, yielding an improved vehicle platoon control accuracy, string stability, and collision avoidance performance.

Stratified Control Strategy of Vehicle Longitudinal Active Collision Avoidance

In order to improve the safety and real-time performance of vehicle longitudinal active collision avoidance, a layered collision avoidance control method based on warning value, standard collision time safety model TTC and fuzzy PID is proposed in this paper. Based on the collision time safety model, the upper controller obtains the braking deceleration of the vehicle according to the relative speed and relative displacement of the obstacle in front. The lower controller is based on fuzzy PID, according to the brake deceleration speed of the upper vehicle, by controlling the pressure of the brake master cylinder of the vehicle, the control of vehicle speed is realized. Finally, using the CarSim/Simulink co-simulation platform, and taking the straight-line working condition as an example, the collision avoidance effect of the controlled vehicle under different speeds was simulated and verified. The results show that the upper controller can afford the appropriate speed of collision avoidance and deceleration according to the reference information, and the lower controller can adjust the brake deceleration speed according to the fuzzy PID, so as to realize the vehicle braking pressure, meet the requirements of longitudinal active collision avoidance.

Infinity-Norm-Based Worst-Case Collision Avoidance Control for Quadrotors

In this paper, we propose the infinity-norm based worst-case collision avoidance control system for quadrotors with the collision detector. The associated worst-case collision avoidance is captured via the infinity-norm in \mathbb R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (equivalently the cube in \mathbb R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). With the proposed infinity-norm based collision avoidance approach, the quadrotors are able to avoid any obstacles that cannot be detected by the standard two-norm in \mathbb R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (the cylinder in \mathbb R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). In our proposed collision avoidance method, the obstacle approximation algorithm approximates the true coordinates of obstacles by simple integer values to reduce computational complexity. Then the approximated obstacle is surrounded by infinity-norm based to cover the actual obstacle. Note that the actual obstacle may not be covered with the standard two-norm, which can cause the collision. We then design the collision detector and the Model Predictive Control (MPC) tracker, where the latter generates the optimal trajectory for the quadrotor by cooperating with the proposed collision detector. The optimal trajectory generated by the MPC tracker is regarded as the reference input for the quadrotor without collision, where the quadrotor is controlled by the modified backstepping controller. The proposed approach is validated by performing the hardware-in-the-loop simulations (HILS) and experiments. Specifically, we show the superiority of the proposed infinity-norm based collision avoidance performance, by comparing it with the standard two-norm based approach in various maneuver situations of the quadrotor.

An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle

Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.

Adaptive Leader–Follower Formation Control of Underactuated Surface Vehicles With Guaranteed Performance

This article studies the formation tracking control problem for a group of underactuated surface vehicles with guaranteed transient properties, including connectivity maintenance, collision avoidance, and tracking performance specifications. The formation is within the leader–follower control framework, in which every follower is controlled to track its leader and maintain a desired relative distance and bearing angle with respect to its leader such that the prescribed formation geometry is achieved based on local sensing capability. The onboard sensor systems are of limited range and angle of view, thus defining a cone of detectable region for every follower. Each follower can detect its leader, if and only if the relative distance and bearing angle keep always inside the predefined detectable region such that the connectivity between the follower and its leader is maintained over time. In addition to the consideration of connectivity maintenance, no collision between the follower and its leader is also considered. A transverse function control approach is introduced to overcome the difficulties caused by the off-diagonal system matrix and underactuation. The barrier Lyapunov function and adaptive backstepping procedure are incorporated into the formation control design to achieve the boundedness of the closed-loop systems with guaranteed transient performance. Collision avoidance and connectivity maintenance between every follower and its leader are also proven mathematically. Simulation studies are performed to show the effectiveness of the proposed control design technique.

대양에서 자율운항선박의 충돌회피 능력 평가를 위한 시나리오 개발에 관한 연구

대양에서 자율운항선박의 충돌회피 능력 평가를 위한 시나리오 개발에 관한 연구

Consensus of multi‐agent system based on novel avoidance function and unequal avoidance strategy

In practice, the energy of an agent is limited, therefore, saving energy is an important problem in the practical applications of multi-agent systems. For this problem, the authors firstly proposes a novel collision avoidance function, which have much less energy cost than the normal potential field function. Secondly, to further reduce energy cost, the authors propose an unequal collision avoidance strategy, that is, according to the residual energy of neighbouring agents, high-energy agent performs collision avoidance, while the low-energy agent does not. Thirdly, the controller is designed based on the novel collision avoidance function and the unequal collision avoidance strategy. Besides the consensus and collision avoidance can be achieved, the proposed controller can also effectively extend the operation time/range of the whole system. Finally, the simulations verify the effectiveness and superiority of the proposed controller.

Multitarget Search of Swarm Robots in Unknown Complex Environments

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning.

It is challenging to avoid obstacles safely and efficiently for multiple robots of different shapes in distributed and communication-free scenarios, where robots do not communicate with each other and only sense other robots’ positions and obstacles around them. Most existing multi-robot collision avoidance systems either require communication between robots or require expensive movement data of other robots, like velocities, accelerations and paths. In this paper, we propose a map-based deep reinforcement learning approach for multi-robot collision avoidance in a distributed and communication-free environment. We use the egocentric local grid map of a robot to represent the environmental information around it including its shape and observable appearances of other robots and obstacles, which can be easily generated by using multiple sensors or sensor fusion. Then we apply the distributed proximal policy optimization (DPPO) algorithm to train a convolutional neural network that directly maps three frames of egocentric local grid maps and the robot’s relative local goal positions into low-level robot control commands. Compared to other methods, the map-based approach is more robust to noisy sensor data, does not require robots’ movement data and considers sizes and shapes of related robots, which make it to be more efficient and easier to be deployed to real robots. We first train the neural network in a specified simulator of multiple mobile robots using DPPO, where a multi-stage curriculum learning strategy for multiple scenarios is used to improve the performance. Then we deploy the trained model to real robots to perform collision avoidance in their navigation without tedious parameter tuning. We evaluate the approach with multiple scenarios both in the simulator and on four differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient and outperforms existing DRL-based approaches in many indicators. We also conduct ablation studies showing the positive effects of using egocentric grid maps and multi-stage curriculum learning.

Real-time collision avoidance planning for unmanned surface vessels based on field theory

With the development of commercial application of unmanned surface vessels, the real-time collision avoidance is the research focus on the multi-vessels situation, the structure complexity and time consumption problem limit its efficiency. To simplify the complex model and enhance the collision avoidance performance, the field theory is used to describe the navigation of ships abstractly, from which a more simplified optimization model of collision avoidance is derived, so the synthetic field regarding the virtual spatial electric field and velocity field is presented to exhibit the situation of ship motion. On the basis of the field environment, the more simplified collision avoidance model is designed, combining with the improved Electromagnetism-like mechanism algorithm, to generate the optimal collision avoidance strategy, for considering the energy and other loss. The complete algorithm has been compared and validated under the different layout, it is worth mentioning that the algorithm can potentially be developed to advance collision-free navigation in multi-vessels situation.

Efficient and Trustworthy Social Navigation via Explicit and Implicit Robot–Human Communication

In this paper, we present a planning framework that uses a combination of implicit (robot motion) and explicit (visual/audio/haptic feedback) communication during mobile robot navigation. First, we developed a model that approximates both continuous movements and discrete behavior modes in human navigation, considering the effects of implicit and explicit communication on human decision making. The model approximates the human as an optimal agent, with a reward function obtained through inverse reinforcement learning. Second, a planner uses this model to generate communicative actions that maximize the robot's transparency and efficiency. We implemented the planner on a mobile robot, using a wearable haptic device for explicit communication. In a user study of an indoor human-robot pair of orthogonal crossing situation, the robot was able to actively communicate its intent to users in order to avoid collisions and facilitate efficient trajectories. Results showed that the planner generated plans that were easier to understand, reduced users' effort, and increased users' trust of the robot, compared to simply performing collision avoidance. The key contribution of this work is the integration and analysis of explicit communication (together with implicit communication) for social navigation.

Utilising bidirectional inequality constraints in optimal robust control for heterogeneous vehicular platoons

This study investigates the robust control problem of a heterogeneous vehicular platoon subject to non-linear and (possibly fast) time-varying uncertainties. The uncertainties are induced by parameter variations and external disturbances. The bound of the uncertainty is described via a continuous function. Firstly, the platoon is modelled as a coupled uncertain dynamic system. To guarantee collision avoidance and compact formation performance, the bidirectional inequality constraints are established for the spacing error between adjacent vehicles. A mathematical transformation scheme is proposed to convert the bounded state into an unbounded one. Then, based on the Udwdia–Kalaba approach and Lyapunov stability theory, a constraint-following robust controller is designed. The controller renders the uniform boundedness and uniform ultimate boundedness performance of the unbounded state, which in turn guarantees the bidirectional restrictions for the spacing error. Moreover, an optimal design scheme for the tunable parameter of this controller is proposed to minimise a comprehensive index involving the system performance and control cost. Finally, numerical simulations are conducted to validate the efficiency of the proposed algorithm.

Velocity Obstacle Based on Vertical Ellipse for Multi-Robot Collision Avoidance

Bounding volume based approaches in velocity obstacle (VO) provide a good solution for collision avoidance of mobile robots with uncertainty. However, the VO built with the bounding footprint always has over-constraining problems which may lead to conservative maneuvers of the mobile robots. Addressing this problem, a vertical ellipse based velocity obstacle (VEVO) collision avoidance method is proposed in this paper. The method mitigates the over-constraining situation by building the footprint probability ellipse whose minor axis is vertical to the direction of the obstacle to minimize the VO area. Based on VEVO, a DWA (Dynamic Window Approach) integrated method is proposed to provide a set of available velocities in speed selection. According to different collision avoidance objectives like collision safety, shortest time consumption and shortest trajectory length, a multi-objective velocity selecting strategy is proposed to provide optimal velocities for motion planning. Furthermore, a dynamic local path adjustment method is proposed to help robots react to the closest obstacle (dynamic or static) according to different collision safety requirements. We validate our methods in a simulated workspace with different numbers of robots going to their goal points. Experimental results show VEVO method could improve the collision avoidance performance in crowded multi-robot environment and robots could achieve their different objectives when suitable parameters are set in the velocity evaluation function. The proposed dynamic local path adjustment method only affects the trajectories in local areas and could ensure collision avoidance safety and performance at the same time.