Collision Avoidance Trajectory Research Articles

Perception-Aware Receding Horizon Trajectory Planning for Multicopters With Visual-Inertial Odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach is able to fly the vehicle to a goal position at high speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory’s aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera’s view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.

DETERMINISTIC-BASED SHIP ANTI-COLLISION ROUTE OPTIMIZATION WITH WEB-BASED APPLICATION

Most of the accidents are caused by human error at sea so, decision making process made by navigators should be more computerised and automated. The supported decision making can be a step forward to decrease the risk of collision. This paper, in this respect, aims to present a deterministic approach to support optimum collision avoidance trajectory. This approach involves a collision avoidance course alteration. A web-based application coded with "JavaScript" programming language on the "Processing" software platform which allows the own ship to change her course in a deterministic manner to avoid collision optimally has been introduced. Algorithm structure of the method has been formulated and organized according to the International Regulation for Preventing Collision at Sea (COLREGs). The experimental tests results have revealed that the system is practicable and feasible and considerably outperforms heuristic-based method. It is thought that the developed method can be applied in an intelligent avoidance system on board and provides contribution to ship collision avoidance process, automation of ship motion control and ship traffic engineering.

Smooth transition for collision avoidance of redundant robots: An on-line polynomial approach

• Smoothing of transientin collision avoidance trajectoryconsidering thecontinuity • Collision avoidance trajectory adjusted by polynomial functions • Systematic formulation of trajectories aiming to decrease the calculation effort • Improvement behavior of transitions as a function of the polynomial order • Using Adaptive Extended Jacobian when the robot is out of collision imminence The presence of cooperation between robots and machines in the industrial environment improved the solution for several manufacturing problems. With cooperation, new challenges emerged, and among these stands out the collision avoidance between such robots and machines. Collision avoidance can be dealt with in several ways, taking into account the computational effort to make a decision and the quality of the calculated trajectory for the robots, evaluated, for instance, by smooth profiles avoiding sudden variations in joints’ velocities or acceleration. In these circumstances, the involved robots need to be redundant since new movements are necessary for avoiding collisions. The strategies for collision avoidance are offline (i.e., based on pre-programming the task), or online (i.e., implemented while the robot performs the main task). In online collision avoidance strategies, numerical performance must ensure the time requirements of the main task performed by the robot; so, numerically efficient solutions are the most appropriate. This paper presents a proposal for the collision avoidance treatment from fixed obstacles for redundant robots, based on polynomial functions. The proposed solution allows achieving smooth trajectories according to criteria based on the continuity of derivatives in trajectory curve transitions. When the robot is out of the imminent collision, it is proposed to solve the inverse kinematics through the Adaptive Extended Jacobians. Throughout the text, the mathematical developments based on polynomials are presented, and in the end, a case study graphically shows comparative results.

Online collision avoidance trajectory planning for spacecraft proximity operations with uncertain obstacle

The spacecraft relative motion trajectory planning is one of the enabling techniques for autonomous proximity operations, especially in the increasingly complicated mission environments. Most traditional trajectory planning methods focus on improving the performance criteria in the deterministic conditions, whereas various uncertain elements in practice would significantly degrade the trajectory performance. Considering the uncertainties underlying the collision avoidance constraints, this paper suggests a model predictive control based online trajectory planning framework in which the obstacle information in higher-precision would be consistently updated by the onboard sensor. To improve the computational efficiency of the online planning framework, the rotating hyperplane (RH) technique is utilized to transform the nonlinear ellipsoidal keep-out zone constraints into convex formulations. And the concept of rotation window is introduced to eliminate the unexpected mismatch between the spacecraft motion and hyperplane rotation in the conventional RH method, which in sequence improves the RH method’s capability for multiple obstacle avoidance problem. Moreover, a three-dimensional (3-D) extension strategy is proposed to simplify the computation procedure when applying the RH method for a 3-D collision avoidance problem. Numerical simulations are carried out to validate the performance of the proposed online trajectory planning framework in addressing the uncertain collision avoidance constraints.

Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

Ship collisions cause major losses in terms of property, equipment, and human lives. Therefore, more investigations should be focused on this problem, which mainly results from human error during ship control. Indeed, to reduce human error and considerably improve the safe traffic of ships, an intelligent tool based on fuzzy set theory is proposed in this paper that helps navigators make fast and competent decisions in eventual collision situations. Moreover, as a result of selecting the shortest collision avoidance trajectory, our tool minimizes energy consumption. The main aim of this paper was the development of a decision-support system based on an artificial intelligence technique for safe ship trajectory determination in collision situations. The ship’s trajectory optimization is ensured by multistage decision making in collision situations in a fuzzy environment. Furthermore, the navigator’s subjective evaluation in decision making is taken into account in the process model and is included in the modified membership function of constraints. A comparative analysis of two methods, i.e., a method based on neural networks and a method based on the evolutionary algorithm, is presented. The proposed technique is a promising solution for use in real time in onboard decision-support systems. It demonstrated a high accuracy in finding the optimal collision avoidance trajectory, thus ensuring the safety of the crew, property, and equipment, while minimizing energy consumption.

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control.

Real-time collision-avoidance navigation of autonomous ships is required by many application scenarios, such as carriage of goods by sea, search, and rescue. The collision avoidance algorithm is the core of autonomous navigation for Maritime autonomous surface ships (MASS). In order to realize real-time and free-collision under the condition of multi-ship encounter in an uncertain environment, a real-time collision avoidance framework is proposed using B-spline and optimal decoupling control. This framework takes advantage to handle the uncertain environment with limited sensing MASS which plans dynamically feasible, highly reliable, and safe feasible collision avoidance. First, owing to the collision risk assessment, a B-spline-based collision avoidance trajectory search (BCATS) algorithm is proposed to generate free-collision trajectories effectively. Second, a waypoint-based collision avoidance trajectory optimization is proposed with the path-speed decoupling control. Two benefits, a reduction of control cost and an improvement in the smoothness of the collision avoidance trajectory, are delivered. Finally, we conducted an experiment using the Electronic Chart System (ECS). The results reveal the robustness and real-time collision avoidance trajectory planned by the proposed collision avoidance system.

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.

A tracking control method for collision avoidance trajectory of autonomous vehicle based on multi-constraint MPC

A tracking control method for collision avoidance trajectory of autonomous vehicle based on multi-constraint MPC

A tracking control method for collision avoidance trajectory of autonomous vehicle based on multi-constraint MPC

A tracking control method for collision avoidance trajectory of autonomous vehicle based on multi-constraint MPC

Local-bearing-information-based unmanned aerial vehicle collision avoidance trajectory planning

Due to its low cost, low power, and non-cooperative advantage, monocular vision has become a crucial solution for enhancing the Sense and Avoid capability of unmanned aerial vehicles (UAVs). However, due to the restricted optical measurement properties of monocular vision, there are drawbacks to monocular-vision-based obstacle perception; namely, the capability for obstacle range information perception and global obstacle perception is insufficient. In this paper, a local-bearing-angle-based trajectory planning algorithm is proposed for UAV collision avoidance. First, the observability of monocular-vision-based obstacle range perception is theoretically analyzed. The local coordinate system is selected for collision avoidance, and the bearing angle is selected as the observation. Second, the problem of UAV monocular vision collision avoidance trajectory planning is mathematically formulated. The quadratic Bezier curve is selected to model the trajectory, and the trajectory planning problem is transformed into optimization based on a single objective function. The collision avoidance constraint based on the relative bearing angle rate is designed, and the collision avoidance trajectory segment is optimized on the basis of the bearing angle observed within a limited time period. Finally, the receding horizon is adopted to optimize the trajectory sequence, and the sequences are connected to formulate the near-globally optimal trajectory. Simulation experiments reveal that the collision avoidance trajectory planning method is capable of avoiding both static and dynamic obstacles. Compared with global-information-based collision avoidance, the convergence time of the method proposed in this paper is reduced, and the near-globally optimal trajectory can be acquired in real time, which is in accordance with the perception capability of monocular vision.

Evasive behavior-based method for threat assessment in different scenarios: A novel framework for intelligent vehicle

The threat assessment process is a crucial part of intelligent vehicles (IVs) for evaluating the levels of criticality and taking possible measures to avoid the collision, especially for the collision avoidance systems (CAS). In this study, a novel threat assessment framework based on the driver’s evasive behavior, namely the CPIC, is proposed, which integrates the crash probability (CP) and inevitable crash (IC) state to be widely used by different CAS in different scenarios. In the first step of the CPIC framework, the detailed evasive driver behavior models (E-DBMs) in the form of probability density functions (PDFs) were introduced to generate more realistic collision-avoidance trajectories. Two techniques for sampling these trajectories, namely the Markov Chain Monte Carlo (MCMC) and adaptive Gaussian mixture framework (GMM) methods, were utilized to ensure the samples were from the area of high probability density in the E-DBMs. The CP value could be derived by considering multiple collision-avoidance trajectories. To confirm the IC state in step 2, the CPIC framework employed the driving limit-based approach for IC checking, which combined the CP value to double-check the unavoidable collision. A total of 82 critical events from the real-world naturalistic driving study, the Strategic Highway Research Program 2 (SHRP2), were extracted to verify the performance of the CPIC framework in different scenarios. Results show that the proposed method clearly revealed the risk levels when two vehicles were approaching, and 80 events were successfully identified as near crashes/crashes. Moreover, the real-time performance of the CPIC framework was also demonstrated. The findings indicate this CPIC framework could be used in practical applications of IVs in different scenarios.

Monocular vision based obstacle avoidance trajectory planning for Unmanned Aerial Vehicle

In recent years, monocular vision has been considered as an effective Unmanned Aerial Vehicle (UAV) onboard obstacle perception solution with applications to Sense and Avoid (SAA). However, with the limitations of monocular optical measurement, monocular vision based obstacle localization ability is insufficient for collision avoidance. Therefore, an obstacle collision avoidance trajectory planning scheme is proposed in this paper with considerations of monocular optical measurement characteristic. Firstly, two obstacle localization modes are defined in this paper to assist with obstacle collision avoidance, namely relative range based Mode 1 and relative angle based Mode 2. Obstacle localization observability of Mode 2 is analyzed, and coordinate systems for obstacle localization are constructed based on the analysis results. Given coordinate systems, Orthogonal Iteration (OI) is further adopted for obstacle localization. Secondly, due to the lack of global knowledge caused by monocular vision based localization capability, a rolling horizon based safe trajectory planning method is presented. In each time segment, the trajectory is optimized with considerations of: 1) objective functions including minimum trajectory length, elapsed time and energy consumption; 2) constraints including Mode 1 and 2 based obstacle collision avoidance constrains and UAV flight constraints. Finally, simulation results indicate the proposed obstacle collision avoidance trajectory planning scheme enhances UAV safety level and can achieve favorable performance when compared with geometric based obstacle collision avoidance and collision avoidance with global knowledge.

Artificial forces for virtual autonomous ships with encountering situations in restricted waters

ABSTRACT Even if the same two ships operate in the same encountering situation, the actual strategies and timing of operations may be different. Therefore, the fixed collision avoidance trajectory and ship movements are no longer suitable for simulating the real ship behaviour. This paper tries to develop an artificial intelligent system that fully sensitive to the local circumstances and command a ship in virtual environment. Based on AIS (Automatic Identification System) data, this study has developed artificial forces which help decision makings on-board independently under different situations and catching the stochastic nature of ship behaviours during collision avoidances manoeuvring. Actual ship tracks are helpful for ascertaining the parameters that contribute to artificial forces in collision avoidances, while the correlation coefficient analysis is helpful to distinguish the parameters. This study will help to develop a navigation traffic simulation to reflect the realistic ship behaviour and provide reliable information for port and waterway planning, risk analysis, and mitigation measures. The method can be used in developing algorithms for autonomous ships. The method introduced in this study lays a foundation for developing artificial forces at intersections or bends, although the model is only applicable in straight channels at the moment.

Fast cooperative trajectory optimization and test verification for close-range satellite formation using Finite Fourier Series method

The process of formation reconfiguration for close-range satellite formation should take into account the risk of collisions between satellites. To this end, this paper presents a method to rapidly generate low-thrust collision-avoidance trajectories in the formation reconfiguration using Finite Fourier Series (FFS). The FFS method can rapidly generate the collision-avoidance three-dimensional trajectory. The results obtained by the FFS method are used as an initial guess in the Gauss Pseudospectral Method (GPM) solver to verify the applicability of the results. Compared with the GPM method, the FFS method needs very little computing time to obtain the results with very little difference in performance index. To verify the effectiveness, the proposed method is tested and validated by a formation control testbed. Three satellite simulators in the testbed are used to simulate two-dimensional satellite formation reconfiguration. The simulation and experimental results show that the FFS method can rapidly generate trajectories and effectively reduce the risk of collision between satellites. This fast trajectory generation method has great significance for on-line, constantly satellite formation reconfiguration.

Distributed Multi-Objective Algorithm for Preventing Multi-Ship Collisions at Sea

Avoidance of collisions at sea is crucial to navigational safety. In this paper, we use a distributed algorithm to communicate the entire collision avoidance trajectory information for each ship. In each communication, we suggest a new improvement function considering safety and efficiency to identify the avoidance ship in each cycle. Considering the nonlinear collision avoidance trajectory of ships, a new method for calculating the degree of danger using a velocity obstacle algorithm is proposed. Therefore, in each communication, each ship considers the avoidance behaviours of other ships in planning its avoidance trajectory. Additionally, we combine bi-criterion evolution (BCE) and the ant lion optimiser to plan the entire collision avoidance path. Three scenarios are designed to demonstrate the performance of this method. The results show that the proposed method can find a suitable collision-free solution for all ships.

Unmanned Surface Vehicle Collision Avoidance Trajectory Planning in an Uncertain Environment

Unmanned surface vehicles (USVs) can encounter undetected moving obstacles while sailing along a planned global path. USVs need to plan collision avoidance trajectories for moving obstacles. In this paper, an algorithm based on the Gaussian mixture model (GMM) and Gaussian process regression (GPR) is proposed to predict the motion of moving obstacles and estimate the uncertainty of the prediction. A nonlinear finite-time velocity obstacle (NLFVO) method is developed for obstacle avoidance. The NLFVO method analyzes the velocity of the USV and the predicted uncertain velocity vectors of the moving obstacles and selects a collision-free velocity for the USV and minimizes the objective function. To enable the actual navigation of USVs, the International Regulations for Preventing Collisions at Sea (COLREGs) are considered in addition to the NLFVO method. The simulation results show that the prediction algorithm can effectively predict the trajectory of moving obstacles, and the NLFVO method can obtain a collision-free trajectory for a USV.

Co-operative Collision Avoidance for Unmanned Aerial Vehicles using both Centralised and Decoupled Approaches

Abstract This paper presents the design of co-operative collision avoidance algorithms for Unmanned Aerial Vehicles (UAVs) using vertical avoidance manoeuvres. The co-operative collision avoidance problem is formulated as an optimal control problem and solved using an A⁎ search algorithm. Two different approaches are developed and compared: a centralised approach where the collision avoidance trajectories for all UAVs are planned simultaneously, and a decoupled approach where the individual collision avoidance trajectories for each UAV is planned sequentially, with the planning sequence determined by a UAV priority order. The UAVs co-operate by sharing their state and intent information with one another and with a central node, if present. The co-operative collision avoidance algorithms are verified and evaluated using illustrative simulations. These simulations support the expected behaviour of the algorithms. The centralised approach finds the most optimal solution to the problem while the solution found by the decoupled approach depends on the priority allocation of the UAVs. The decoupled approach can find either the most optimal or a sub-optimal solution to the problem, with the priority allocation occasionally resulting in the decoupled approach being unable to find a solution. This suggests that the centralised approach will, on average, find solutions more often and find more optimal solutions than the decoupled approach.

Deterministic-Based Ship Anti-Collision Route Optimization with Web-Based Application

Most of the accidents are caused by human error at sea so, decision making process made by navigators should be more computerised and automated. The supported decision making can be a step forward to decrease the risk of collision. This paper, in this respect, aims to present a deterministic approach to support optimum collision avoidance trajectory. This approach involves a collision avoidance course alteration. A web-based application coded with "JavaScript" programming language on the "Processing" software platform which allows the own ship to change her course in a deterministic manner to avoid collision optimally has been introduced. Algorithm structure of the method has been formulated and organized according to the International Regulation for Preventing Collision at Sea (COLREGs). The experimental tests results have revealed that the system is practicable and feasible and considerably outperforms heuristic-based method. It is thought that the developed method can be applied in an intelligent avoidance system on board and provides contribution to ship collision avoidance process, automation of ship motion control and ship traffic engineering.

Method of Emergency Collision Avoidance for Unmanned Surface Vehicle (USV) Based on Motion Ability Database

Abstract The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance

With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.