Inter-vehicle Collision Avoidance Research Articles

Robust collision-free formation control of quadrotor fleets: Trajectory generation and tracking with experimental validation

This paper addresses the problem of formation control of multiple quadrotor vehicles, focusing on ensuring inter-vehicle collision avoidance in the presence of time-varying external disturbances. The problem is divided into two sub-problems: (i) the generation of collision-free trajectories; and (ii) trajectory tracking. Initially, by employing the Lyapunov-based backstepping technique, a set of collision-free trajectories is generated based on a potential function. Additionally, a trajectory tracking controller is developed for each individual quadrotor, ensuring accurate tracking of their respective trajectories. Furthermore, to attain robust tracking performance, nonlinear disturbance observers are developed and incorporated into the control inputs to compensate for time-varying external disturbances. Comprehensive simulation and experimental results are provided and analyzed to demonstrate the effectiveness and performance of the proposed strategy.

Cooperative Constrained Control of Autonomous Vehicles With Nonuniform Input Quantization

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions.

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

Formation Reconstruction and Trajectory Replanning for Multi-UAV Patrol

This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. Unlike most of the formation flying related work, this article considers a more practical issue that some of the vehicles may break down during operation. In this case, a more reasonable coping strategy is proposed which is to reconstruct the formation such that the task objectives can be achieved optimally. To perform the patrol task, a virtual target is introduced which moves along the patrol path with a predetermined speed. Considering the fact that not all the vehicles have access to the patrol path information, a decentralized estimator is designed for each vehicle to estimate the virtual target state respectively based on which the individual reference trajectories can be generated. As these reference trajectories do not satisfy relevant physical constraints, including system model, control input limits, no-fly zone avoidance, and intervehicle collision avoidance, a novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which is computationally attractive by incorporating a situation-dependent mechanism. Simulations have been conducted to validate the effectiveness of our proposed algorithm.

Trajectory planning and control for drone replacement for multidrone cinematography

This work addresses the problem of trajectory planning and control for a team of drones, so that a vehicle that is following a target as part of a multi-vehicle formation flight can be safely replaced with minimum impact on the formation objectives. An optimal trajectory generation method is presented, which relies on formulating a nonlinear optimization problem with constraints imposed by the physical limitations of the vehicle, the formation, and the surrounding environment, including nonlinear constraints to enforce inter-vehicle collision avoidance and clear visibility of the target. An online planning and control strategy is also implemented through a Nonlinear Model Predictive Control problem, based on the dynamic model of the system, a quadratic objective function, and a set of constraints, that again include collision avoidance and clear visibility. The proposed algorithms are validated in software-in-the-loop simulations that include the autopilot software.

Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows

A multi-vehicle flocking and guidance control scheme is proposed for small autonomous underwater vehicles in the presence of strong ocean flows that exceed vehicles’ actuation capabilities. The flocking problem and flock guidance problem are simultaneously addressed by enduing fluid properties to the vehicle swarm. This control scheme generates cohesive flocking behavior with inherent inter-vehicle collision avoidance and velocity consensus. The vehicle flock is guided along a fuel-optimal trajectory calculated for an individual vehicle based on background flow predictions, allowing us to compute a single optimal trajectory while still achieving robust flocking performance and near optimality for all vehicles. Resultant vehicle trajectories are nearly fuel-optimal when the spatial variation of background flow velocities across the flock is small. Dimensional analysis uncovers two important independent parameters dictating the balance between swarm compressibility and the degree of velocity consensus. The efficiency of the presented method is demonstrated in simulations with two synthetic background flow fields resembling realistic ocean flow patterns, and a flow field reconstructed based on ocean model data. A quantitative comparison with a generic artificial potential based control scheme shows that, owing to the inherent velocity consensus effect, the proposed method results in better flocking behavior and less actuation energy consumption.

Range-based cooperative underwater target localization

The paper addresses the problem of range-based cooperative underwater target localization. In its simplest form, target localization aims to estimate the position of a stationary or mobile target by using range measurements between the target and a vehicle, called a tracker, undergoing a trajectory that can be measured on-line. In this context, the tracker must execute sufficiently exciting maneuvers so as to maximize the range-based information available for target localization. The paper extends this set-up to the case where the tracker works in cooperation with another vehicle, called companion, that can also measure ranges to the target and shares this info with the tracker. The latter may have access to the position of the companion or, in some cases, only to the range between the two vehicles. We consider three different operating scenarios where the motion of the tracker is chosen so as to increase the accuracy with which the position of the target can be estimated. The scenarios reflect the situations where the motion of the companion vehicle satisfies one of three conditions: i) the motion is not defined a priori and can also be optimized, ii) the motion is fixed a priori and is known to the tracker (scenario in which the tracker benefits from the extra information acquired by the companion vehicle, which tracks a desired trajectory in the context of a separate, independent mission), and iii) the motion is not known a priori and must be learned in the course of the mission. By adopting an estimation-theoretical settings, optimal trajectories are computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM), subjected to vehicle maneuvering constraints and inter-vehicle collision avoidance. Simulation results illustrate the methodology adopted for cooperative target localization.

Adaptive Beaconing for Effective Inter-Vehicle Collision Avoidance System

Cooperative awareness among vehicles is required to make driving safer. Inter-vehicle communication is achieved by beaconing in the vicinity of the respective vehicles. The beaconing includes the exchange of periodic messages on each vehicle’s status information. The shorter the period of the beacon message transmission is, the higher the status information accuracy is. Accordingly, driving is safer. However, frequent exchanges of the periodic beacon cause wireless channel congestion and packet loss. To address the trade-off problem between the accuracy of information and the channel load, this paper presents a scheme to determine the optimum beacon interval for guaranteeing fundamental accuracy, while maintaining a moderate load on the communication channel. A performance evaluation using a computer simulation showed that the proposed scheme guarantees sufficient information accuracy for effectively avoiding inter-vehicle collisions and information overload. Furthermore, the proposed scheme is compatible with, and can be directly applied to, the current vehicular ad hoc network system implementation.

Distributed Formation and Reconfiguration Control of VTOL UAVs

In this brief, a novel distributed cascade robust feedback control approach is proposed for formation and reconfiguration control of a team of vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). This approach is based on dynamic communication network. It guarantees intervehicle collision avoidance and considers dynamic constraints of UAVs. In the outer loop of the cascade formation control, a potential field approach is used to generate a desired velocity for each UAV, which ensures that the team of UAVs can perform formation flying, formation rotating and reconfiguration, avoid intervehicle collision, as well as track a specified virtual leader. In the inner loop of the cascade formation control, the velocity of each UAV is designed to track its desired velocity generated by the outer loop, subject to dynamic constraints. The proposed approach is demonstrated via both simulation and flight test.

Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

ABSTRACTIn this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.

Intelligent Forecasting Using Dead Reckoning With Dynamic Errors

A method for integrating, processing, and analyzing sensing data from vehicle-mounted sensors for intelligent forecasting and decision-making is introduced. This dead reckoning with dynamic errors (DRWDEs) is for a large-scale integration of distributed resources and sensing data intervehicle collision avoidance system. This sensor fusion algorithm is introduced to predict the future trajectory of a vehicle. Current systems that predict a vehicle's future trajectory, necessary in a network of collision avoidance systems, tend to have a lot of errors when the vehicles are moving in a nonstraight path. Our system has been designed with the objective of improving the estimations during curves. To evaluate this system, our research uses a Garmin 16HVS GPS sensor, an AutoEnginuity OBDII ScanTool, and a Crossbow three-axis accelerometer. Using Kalman filters (KFs), a dynamic noise covariance matrix merged together with an interacting multiple models (IMMs) system, our DRWDE produces the future position estimation of where the vehicle will be 3 s later in time. The ability to handle the change in noise, depending on unavailable sensor measurements, permits a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared with a more common KF implementation that runs at the rate of its slowest sensor (1 Hz in our setup), our experimental results showed that our DRWDE (running at 10 Hz) yielded more accurate predictions (25%-50% improvement) during abrupt changes in the heading of the vehicle.

Integrated Motion Planning, Control, and Estimation for Range-Based Marine Vehicle Positioning and Target Localization

Abstract: The paper addresses the problems of range-based marine vehicle positioning and target localization. Vehicle positioning aims to estimate the positions of one or more vehicles from a sequence of range measurements to fixed or moving acoustic beacons with known locations. In this context, the vehicles must execute sufficiently exciting maneuvers so as to maximize the range-based information available for multiple vehicle positioning. Using an estimation theoretical setting, the vehicle trajectories are computed by maximizing the determinant of a suitably defined Fisher information matrix (FIM), subject to inter-vehicle collision avoidance and vehicle maneuvering constraints. A numerical solution is proposed for the general case. Analytical solutions are obtained in the case of one vehicle and one beacon, when the latter undergoes trajectories that are straight lines, pieces of arcs, or a combination thereof. The theoretical analysis is complemented with practical experiments that focus on the dual problem of underwater target localization. The objective is to estimate the position of a moving underwater target by using range measurements between the target and a vehicle, called a tracker, undergoing a trajectory that can be measured on-line. The experimental set-up includes a surface and an autonomous underwater vehicle of the MEDUSA*-class playing the roles of tracker and target, respectively. In the methodology adopted for system implementation the tracker runs three key algorithms simultaneously, over a sliding time window: i) tracker motion planning, ii) tracker motion control, and iii) target motion estimation based on range data acquired on-line.

Swarm model for cooperative multi‐vehicle mobility with inter‐vehicle communications

A novel model is proposed to formulate the mobility of multiple vehicles with inter-communications moving together as a swarm. This model is inspired by the aggregate motion of social force, and is used to describe the self-organised behaviour of the vehicle swarm in vehicular ad hoc networks. To validate and analyse the proposed model, some simulations are performed in different traffic scenarios, such as the low traffic density with high mobility and the high traffic density with low mobility. Two comparative simulation cases are also provided to explore the effect of vehicular communications on the mobility of multiple vehicles. The simulation results prove that the model can well formulate the cooperative behaviours of multiple vehicles with vehicle-to-vehicle communications in inter-vehicle collision avoidance and obstacle-avoidance scenarios, and these results also strengthen that the inter-vehicular communications can enhance the vehicle safety and traffic efficiency.

Cooperative Visibility Maintenance for Leader–Follower Formations in Obstacle Environments

Vision-based formation control of multiple agents, such as mobile robots or fully autonomous cars, has recently received great interest due to its application in robotic networks and automated highways. This paper addresses the cooperative motion coordination of leader-follower formations of nonholonomic mobile robots, under visibility and communication constraints in known polygonal obstacle environments. We initially consider the case of N = 2 agents moving in L-F fashion and propose a feedback control strategy under which L ensures obstacle avoidance for both robots, while F ensures visibility maintenance with L and intervehicle collision avoidance. The derived algorithms are based on set-theoretic methods to guarantee visibility maintenance, dipolar vector fields to maintain the formation shape, and the consideration of the formation as a tractor-trailer system to ensure obstacle avoidance. We furthermore show how the coordination and control design extends to the case of N > 2 agents, and provide simulation results, which demonstrate the efficacy of the control solutions. The proposed algorithms do not require information exchange among robots, but are instead based on information locally available to each agent. In this way, the desired tasks are executed and achieved in a decentralized manner, with each robot taking care of converging to a desired configuration, while maintaining visibility with its target.

Local pursuit strategy-inspired cooperative trajectory planning algorithm for a class of nonlinear constrained dynamical systems

Cooperative trajectory planning is crucial for networked vehicles to respond rapidly in cluttered environments and has a significant impact on many applications such as air traffic or border security monitoring and assessment. One of the challenges in cooperative planning is to find a computationally efficient algorithm that can accommodate both the complexity of the environment and real hardware and configuration constraints of vehicles in the formation. Inspired by a local pursuit strategy observed in foraging ants, feasible and optimal trajectory planning algorithms are proposed in this paper for a class of nonlinear constrained cooperative vehicles in environments with densely populated obstacles. In an iterative hierarchical approach, the local behaviours, such as the formation stability, obstacle avoidance, and individual vehicle's constraints, are considered in each vehicle's (i.e. follower's) decentralised optimisation. The cooperative-level behaviours, such as the inter-vehicle collision avoidance, are considered in the virtual leader's centralised optimisation. Early termination conditions are derived to reduce the computational cost by not wasting time in the local-level optimisation if the virtual leader trajectory does not satisfy those conditions. The expected advantages of the proposed algorithms are (1) the formation can be globally asymptotically maintained in a decentralised manner; (2) each vehicle decides its local trajectory using only the virtual leader and its own information; (3) the formation convergence speed is controlled by one single parameter, which makes it attractive for many practical applications; (4) nonlinear dynamics and many realistic constraints, such as the speed limitation and obstacle avoidance, can be easily considered; (5) inter-vehicle collision avoidance can be guaranteed in both the formation transient stage and the formation steady stage; and (6) the computational cost in finding both the feasible and optimal solutions is low. In particular, the feasible solution can be computed in a very quick fashion. The minimum energy trajectory planning for a group of robots in an obstacle-laden environment is simulated to showcase the advantages of the proposed algorithms.

Discussion on: “Control and navigation in manoeuvres of formations of unmanned mobile vehicles”

Control of a multi-agent system has recently triggered a lot of research interest due to the inherently present problem complexity as well as various interesting applications that have become attractive in last few years, in which such an system can be utilized. The coverage problem, persistent monitoring, sensor networks, surveillance, formation control, highway and transportation systems are some of the possible application areas. Some relevant work can be found in the reference lists presented in [1,2]. Formation control has been a focus of the paper and it can be interpreted as a problem in which a set of mobile vehicles follows a reference trajectory while keeping a priori defined formation shape. Such a problem arises in some military applications as well as in applications like the one mentioned in the paper related to the airport snow shoveling. There are currently three different strategies adopted to solve the formation control problem: leader– follower approach, behavioral approach and virtual structure approach. In the paper, the leader–follower strategy is implemented based on a receding horizon control scheme (RHC). Some relevant work on the field of formation control can be found in the reference list presented in the paper. The main contributions of the paper are resulted by some inherent RHC framework features, providing a general approach which takes into account nonholonomic vehicles’ formation stabilization, a trajectory planning which guarantees task completion and a framework which handles dynamic changes in the environment. In addition, the RHC is enhanced by the possibility of the backward movement of the formations of unmanned mobile vehicles. The backward movement is accomplished by extending the leader– follower strategy with the idea of two virtual leaders which seems a reasonable way to solve this problem. Using an RHC scheme to control a formation of unmanned mobile vehicles allows for taking into account local measurements, which in turn makes the control capable of appropriately responding to dynamical changes in the environment during the mission of the mobile robots. The proposed RHC scheme includes mobility, environment and shape of the formation constraints to find suboptimal trajectories toward the target region. An additional term is also used in the objective function to ensure intervehicle collision avoidance. A capability of a backward movement of formations of unmanned mobile vehicles provides a wide range of possible applications in which the proposed control approach can be utilized. Such an additional feature makes the formations of unmanned mobile vehicles more flexible and capable for some complex applications such as the airport snow shoveling mentioned in the paper. However, the surveillance and the persistent monitoring coverage problem with a formation might be certainly enhanced by the backward movement feature. The proposed navigation framework extends the work [3] in that a detailed theoretical overview of the proposed method is covered jointly with some new experimental results. The paper additionally provides the analysis of the task completion, inherently implied by the convergence of the system into the desired target region. In the sequel, some different possible extensions of the

UAV Formation Flight Based on Nonlinear Model Predictive Control

We designed a distributed collision‐free formation flight control law in the framework of nonlinear model predictive control. Formation configuration is determined in the virtual reference point coordinate system. Obstacle avoidance is guaranteed by cost penalty, and intervehicle collision avoidance is guaranteed by cost penalty combined with a new priority strategy.

Cooperative Motion Planning for Multiple Autonomous Marine Vehicles

There is widespread interest in the deployment of fleets of marine robots with the potential to roam the oceans freely and collect data at an unprecedented scale. This calls for the development of efficient algorithms for multiple vehicle motion planning that can take directly into account the capabilities of each vehicle and environmental conditions and lend themselves to seamless integration with control and navigation systems. The paper describes advances towards the development of a new breed of motion planning systems that address explicitly inter-vehicle collision avoidance, together with a number of criteria that may include simultaneous times of arrival at assigned target points, energy minimization, and acoustic communication constraints. The theoretical framework adopted is rooted in the so-called Projection Operator Approach that borrows from optimization and dynamical systems theory. Simulations with full dynamic vehicle models illustrate the potential of the methods developed.

A DISJUNCTIVE PROGRAMMING APPROACH FOR MOTION PLANNING OF MOBILE ROUTER NETWORKS

In this paper we develop a framework based on disjunctive programming for motion planning of robotic networks. Although the methodology presented in this paper can be applied to general motion planning problems we focus on coordinating a team of mobile routers to maintain connectivity between a fixed base station and a mobile user within a walled environment. This connectivity management problem is decomposed into three steps: (i) a feasible line-of-sight path between the base station and the mobile user is computed; (ii) the number of required routers and their goal locations are determined; and (iii) the motion planning with obstacle and inter-vehicle collision avoidance problem is solved. To illustrate the flexibility of the proposed approach we also formulate a novel motion planning algorithm for a team of mobile robots as a disjunctive program. Cell decomposition is used to take into account the size and orientation of the robots. In both cases, connectivity and motion planning, the mixed-integer optimization problems are solve using CPLEX. Moreover, the proposed approach can easily accommodate input and other constraints and mission objectives. Simulation results show the applicability of the proposed strategy.

Temporally and Spatially Deconflicted Path Planning for Multiple Autonomous Marine Vehicles

There is currently a surge of interest in the development of advanced systems for cooperative control of multiple autonomous marine vehicles. Central to the implementation of these systems is the availability of efficient algorithms for multiple vehicle path planning that can take explicitly into account the capabilities of each vehicle and existing environmental conditions. Multiple vehicle path planning methods build necessarily on key concepts and algorithms for single vehicle path following. However, they go one step further in that they must explicitly address inter-vehicle collision avoidance, together with a number of criteria that may include simultaneous times of arrival at the assigned target points and energy minimization, to name but a few. As such, they pose considerable challenges both from a theoretical and practical implementation standpoint. This paper is a short overview of multiple vehicle path planning techniques. The exposition is focused on specific algorithms developed in the scope of research work in which the authors have participated. Namely, algorithms that ensure that at no time will two vehicles get closer in space than a desired safety distance, that is, achieve trajectory deconfliction. The algorithms make ample use of direct optimization methods that lead to efficient and fast techniques for path generation using a polynomial-based approach. The paper affords the reader a fast paced presentation of key algorithms that had their genesis in the aircraft field, discusses the results of simulations, and suggests problems that warrant further consideration.