Collision Avoidance Control Research Articles

Virtual Fluid-Flow-Model-Based Lane-Keeping Integrated With Collision Avoidance Control System Design for Autonomous Vehicles

The automated steering control technology is crucial for autonomous vehicles, and lane-keeping control systems have been developed extensively, but various traffic conditions, and obstacles ahead of the ego vehicle would cause serious traffic collision accidents. Therefore, this paper proposes a virtual fluid-flow-model (VFFM) based lane-keeping integrated with collision avoidance control system (LKCA) to realize the function of both lane-keeping and collision avoidance. Firstly, a novel lane-keeping model is proposed which is inspired by viscous fluid flowing between two parallel plates or around a cylinder. Then, based on the proposed VFFM-based lane-keeping model, the ego vehicle’s path can be planned, and it ensures the performance of lane-keeping and obstacle-avoidance. Finally, an optimal-preview-driver-model-based path tracking controller is designed to track the desired VFFM-based path. The proposed control system is evaluated by both co-simulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed control system, and it can ensure lane-keeping performance on the premise of obstacle-avoidance.

Composite formation flying strategy for distributed space telescopes with an ultralarge aperture

There is increasing demand for space telescopes with ultralarge apertures in the order of 100 to 1000 m for space exploration. It is difficult to expand a space telescope beyond 100 m because of the extensive structural controls that are needed to satisfy optical interference. Large apertures can be achieved using classical formation flying satellites, but it is difficult to achieve suitable relative position and altitude control. Therefore, a novel composite formation flying strategy for distributed space telescopes that provides an ultralarge aperture and ultrahigh accuracy is presented. The proposed strategy combines coarse and fine formations (FFs) provided by support and payload modules (PMs), respectively. The support and PMs are actively connected by noncontact voice coil actuators (VCAs) in each formation satellite. A model and a VCA collision avoidance control approach are also provided for the proposed satellites. The coarse formation is controlled by small thrusters and gives the relative position accuracy in the order of centimeters. The FF is controlled by the VCAs and gives the relative position accuracy in the order of micrometers. The performance is evaluated using simulation studies.

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR.

Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware module. The hardware module is used to control visually-guided navigation behavior of the stick insect-like hexapod robot HECTOR. By leveraging highly parallelized bio-inspired algorithms to extract nearness information from visual motion in dynamically reconfigurable logic, HECTOR is able to navigate to predefined goal positions without colliding with obstacles. The system drastically outperforms CPU- and graphics card-based implementations in terms of speed and resource efficiency, making it suitable to be also placed on fast moving robots, such as flying drones.

Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach

This letter studies a distributed collision avoidance control problem for a group of rigid bodies on a sphere. A rigid body network, consisting of multiple rigid bodies constrained to a spherical surface and an interconnection topology, is first formulated. In this formulation, it is shown that motion coordination on a sphere is equivalent to attitude coordination on the 3-dimensional Special Orthogonal group. Then, an angle-based control barrier function that can handle a geodesic distance constraint on a spherical surface is presented. The proposed control barrier function is then extended to a relative motion case and applied to a collision avoidance problem for a rigid body network operating on a sphere. Each rigid body chooses its control input by solving a distributed optimization problem to achieve a nominal distributed motion coordination strategy while satisfying constraints for collision avoidance. The proposed collision-free motion coordination law is validated via simulation.

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.

Reinforcement learning based two-level control framework of UAV swarm for cooperative persistent surveillance in an unknown urban area

Persistent surveillance in a complex unknown urban area by an unmanned aerial vehicle (UAV) swarm is a low-cost, promising future application for anti-terrorism, disaster monitoring, and battlefield situational awareness. Based on over-simplified simulated surroundings and a UAV dynamic model, a few remarkable approaches have been proposed; however, they typically rely on non-sensor-based inputs and prior knowledge on the environment or targets. To overcome these limitations, based on simulated city blocks, a two-level quasi-distributed control framework is proposed for realizing the continuous control of a UAV swarm in two defined surveillance phases. With the support of a well-trained and corrected artificial neural network (ANN) in low-level UAV manoeuvre control for target homing and collision avoidance, several preliminary high-level target allocation strategies are designed for a cooperative overall objective based on the synchronization of local surveillance data. Then, via a series of numerical simulations, an optimal high-level strategy combination is identified. Finally, the surveillance performance of this strategy combination is evaluated under various swarm sizes and UAV launching patterns. The simulation results demonstrate that the proposed control framework is applicable for UAV swarm control in the persistent surveillance of unknown urban areas.

Safe Coverage of Compact Domains For Second Order Dynamical Systems

Abstract Autonomous systems operating in close proximity with each other to cover a specified area has many potential applications, but to achieve effective coordination, two key challenges need to be addressed: coordination and safety. For coordination, we propose a locally asymptotically stable distributed coverage controller for compact domains in the plane and homogeneous vehicles modeled with second order dynamics with bounded input forces. This control policy is based on artificial potentials designed to enforce desired vehicle-domain and inter-vehicle separations, and can be applied to arbitrary compact domains including non-convex ones. We prove, using Lyapunov theory, that certain coverage configurations are locally asymptotically stable. For safety, we utilize Hamilton-Jacobi (HJ) reachability theory to guarantee pairwise collision avoidance. Rather than computing numerical solutions of the associated HJ partial differential equation as is typically done, we derive an analytical solution for our second-order vehicle model. This provides an exact, global solution rather than an approximate, local one within some computational domain. In addition to considerably reducing collision count, the collision avoidance controller also reduces oscillatory behaviour of vehicles, helping the system reach steady state faster. We demonstrate our approach in two representative simulations involving a square domain and a non-convex moving domain.

Research on Collision Avoidance Control of Multi-Arm Medical Robot Based on C-Space

A multi-arm robot for mandible reconstruction assisted surgery is developed. It has three manipulators and eighteen degrees of freedom. In order to prevent the collision of the manipulator caused by the overlap of the motion space of the manipulator during the space positioning of the whole mandible reconstruction operation, a collision avoidance path planning method for manipulator based on C-space is proposed. In order to obtain the space non-interference posture of one manipulator, and the interference posture of the other manipulators is taken as the obstacle mapping in C-space to obtain the free space after removing obstacles. At the same time, combining with the space position of the system manipulator, the C-space mapping mathematical model of feature points and line segments is studied, and the mapping mathematical model of any point and line segment in the plane is obtained. By introducing admissibility proof lemma to weigh the evaluation function, the improved heuristic A* search algorithm is used to search the best path for collision avoidance. The collision avoidance experiment is carried out through simulation and robot platform, and finally the animal experiment is carried out. These experimental results verify the feasibility and validity of the collision avoidance path planning method for the space manipulator.

Model predictive control for stabilisation of vehicle nonlinear dynamics to avoid the second collision accident

ABSTRACTThis paper presents an automatic collision avoidance method based on model predictive control (MPC) for nonlinear vehicle dynamics. MPC is a type of optimal feedback control in which the control performance over a finite future is optimised. The C/GMRES algorithm is used for solving the nonlinear model predictive control problem within a short sampling period. A nonlinear tire model is employed to represent the realistic behaviour of a vehicle. The objective of this paper is to propose a nonlinear model predictive control method with a fast numerical solution algorithm called C/GMRES for designing an automatic collision avoidance control system. The effectiveness of the proposed method is verified by numerical simulation.

Fast marching square method based intelligent navigation of the unmanned surface vehicle swarm in restricted waters

Considering the limitations of a single unmanned surface vehicle (USV) in operations, such as a small mission area, lacking system autonomy and insufficient fault-tolerant resilience, there is an increasing interest in developing multiple USVs as a formation fleet in both civilian and naval areas. In order to enhance the autonomy of USVs and effectively achieve missions, an intelligent path planning algorithm and a collision avoidance control method of the USV swarm in restricted waters are presented in this paper. In terms of the path planning of the USV formation fleet, a fast marching square (FMS) method is utilized to generate an optimal path. Based on the path calculated by the FMS algorithm, collision avoidance behaviors are designed according to the International Regulations for Preventing Collisions at Sea (COLREGs), an international maritime collision avoidance regulation, to handle the collision avoidance between the USV swarm and other vessels sailing on the sea. The algorithms have been validated through computer-based simulations, which point out that the path is feasible and the USV swarm can avoid static obstacles as well as moving vessels.

Self-learning control for coordinated collision avoidance of automated vehicles

For the improvement of automotive active safety and the reduction of traffic collisions, significant efforts have been made on developing a vehicle coordinated collision avoidance system. However, the majority of the current solutions can only work in simple driving conditions, and cannot be dynamically optimized as the driving experience grows. In this study, a novel self-learning control framework for coordinated collision avoidance is proposed to address these gaps. First, a dynamic decision model is designed to provide initial braking and steering control inputs based on real-time traffic information. Then, a multilayer artificial neural networks controller is developed to optimize the braking and steering control inputs. Next, a proportional–integral–derivative feedback controller is used to track the optimized control inputs. The effectiveness of the proposed self-learning control method is evaluated using hardware-in-the-loop tests in different scenarios. Experimental results indicate that the proposed method can provide good collision avoidance control effect. Furthermore, vehicle stability during the coordinated collision avoidance control can be gradually improved by the self-learning method as the driving experience grows.

Abstraction-Based Safety Verification and Control of Cooperative Vehicles at Road Intersections

This article considers the problem of designing a centralized controller for vehicle collision avoidance at road junctions and intersections. The controller supervises a set of vehicles, and overrides their inputs when necessary to prevent side and rear-end collisions. By supervising vehicles, rather than taking full control, we obtain a system that can work with semiautomated human-driven vehicles. The price to pay is in complexity: an override is only necessary if, without an intervention, all future input signals will result in a collision. Thus, deciding overrides requires verification of the full reachability set, rather than the computation of a single collision-free trajectory. Our approach to speeding this step up is to use an abstraction of the (concrete) system, which is suitably discretized to obtain a mixed-integer programming problem. We deduce the solution of the original verification problem (VP) from that of the abstraction-based VP by proving an approximate simulation relation between the abstract and concrete systems. The resulting supervisor provably guarantees safety of the concrete system. We also evaluate the approximation error of the supervisor due to the use of an abstraction. Computer simulations show that the supervisor exhibits computationally better performances than other existing controllers applicable to realistic intersection scenarios.

Cooperative Avoidance Control With Velocity-Based Detection Regions

Guaranteed collision avoidance control laws for multi-agent systems typically rely on constant detection regions. This constraint tends to generate conservative and slower agents’ trajectories. To reduce the conservatism of avoidance control laws, this letter presents two decentralized, cooperative strategies for arbitrarily large groups of agents that decrease the vehicles’ effective detection regions by using velocity information. The vehicles are modeled as a class of nonlinear Lagrangian systems which full state vector represents absolute position. The control laws are proven to guarantee collision avoidance at all times and are shown to be more energy-efficient and to generate faster and smoother trajectories than traditional methods. Moreover, by decreasing the detection regions, the agents are able to converge to destinations closer to each other’s avoidance regions, a feature not possible with traditional avoidance control.

Whole-body collision avoidance control design using quadratic programming with strict and soft task priorities

Despite safe mechanical design is necessary for the collaborative robots, we can not underestimate the importance of active safety due to a multi-objective control design. Active safety not only complements the mechanical compliance but also enables classical industrial robots the ability to fulfill additional task-space objectives. Using the gradient of the collision avoidance task as hard constraints of a quadratic programming (QP) controller, we assign strict priority to avoid collisions and specify other QP controller objectives with soft task priorities. Through experiments performed on a dual-arm robot, we show that the proposed solution is able to generate safe robot motion that fulfills the task specifications while keeping the feasibility of the underlying quadratic optimization problem.

Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.

Bounded flight and collision avoidance control for satellite clusters using intersatellite flight bounds

Abstract A novel satellite cluster control method by means of a set of artificial potential functions is proposed in this paper. To avoid the huge difficulty of designing target configuration for a large-scale satellite cluster, the proposed cluster control method relies on intersatellite flight bounds so that satellites can autonomously converge to a given boundary range, rather than a set of exact orbit configuration. The analytic relative motion bounds in terms of relative orbital elements are deduced and used as variables in artificial potential functions. The accumulated control effort is minute, that shows great potential in micro satellite cluster control. On the other hand, collision avoidance as a key requirement for satellite cluster, also gets attention in this paper. For a long-term perspective, the minimum intersatellite flight bound rather than the real-time relative distance between satellites is introduced into the repulsive artificial potential function. Satellite members can adjust their control effort in real time according to nearby satellites, which reflect the characteristic of swarm intelligence. Monte Carlo simulation reveals the superiority in fuel consumption of the proposed collision avoidance control.

Configuration design and collision avoidance control of an electromagnetic vibration isolation system on satellites considering environmental disturbances

This paper presents an electromagnetic vibration isolation system (EVIS) that connects the support module (SM) and the payload module (PM) by electromagnetic interaction, where there is no mechanical connection between the two parts to keep the PM free from disturbances while working on orbit. The configuration and the control method of the EVIS are designed to realize vibration isolation, collision avoidance and accurate control for the PM. First, the configuration of the EVIS is presented and the relative motion equations between the SM and the PM including translation and rotation equations are built, considering disturbances from the SM and space environment. Based on the dynamic equations and the collision constraint between the SM and the PM, the control method which realizes collision avoidance and accurate control for relative motion of the two parts is presented. Finally, the accuracy and efficiency of this study are validated through numerical simulations.

A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

Lane change maneuvers, are important contributors to road traffic accidents on highway. In this paper, we propose a new safe lane change trajectory model and collision avoidance control method for Society of Automotive Engineers (SAE)-level 2 automatic driving vehicles. First, a Gaussian distribution is used to describe the new trajectory model that uses pure steering and combined braking. According to regional and progressive states, a new safe lane change meaning is defined. Second, we design a new four-level automatic driving mode and an effective decision mechanism that considers safety and ergonomics. Moreover, a new trajectory tracking controller combined with a decision mechanism was designed using feedback linearization, verified using typical lane-change scenarios. Finally, based on a physical simulation platform, PreScan, the hardware-in-the-loop simulation result demonstrate the feasibility and effectiveness of our method. This paper provides a valuable reference on an expert and intelligent system methodology for automatic driving vehicles, which will be helpful for improving highway traffic safety and efficiency.

Globally Stable Formation Control of Nonholonomic Multiagent Systems With Bearing-Only Measurement

In this article, the formation control problem of multiagent systems with nonholonomic constraints by bearing-only measurements is studied. Many previous works used the notation of infinitesimal rigidity to define the communication topology in formation control, but the global stability is difficult to achieve without leaders. To overcome this shortage, the maximal clique graph is used to define the topological relationship between agents. By minimizing the designed cost function, a novel distributed bearing-only formation controller is designed. Then, the close-loop system can achieve the global stability due to the existence of common agents between the maximal cliques under the designed controller. Besides, the global collision avoidance control strategy is also investigated with assistant distance measurement, which can prevent collisions between agents during the whole formation process. In addition, the embedded rotation angles between the final formation and the target formation are presented to optimize the motion process in real time. Finally, the simulation results verify the effectiveness of the designed controllers.

Modeling and collision avoidance control for the Disturbance-Free Payload spacecraft

The Disturbance-Free Payload (DFP) spacecraft, which includes the payload module (PM) and the support module (SM) connecting through voice coil motors (VCM), can provide unprecedented control and motion stability for the sensitive payload. The payload module and the support module have a high collision probability because of close proximity formation. In order to perform collision avoidance control on two modules, this paper establishes the dynamics model of the DFP spacecraft and proposes a collision avoidance control strategy based on the model predictive control (MPC). The established model synthesizes the back-Electromotive-Force (back-EMF) of the VCM and the stiffness and damping of the flexible cables. The MPC controller converts the collision avoidance problem into a quadratic programming problem. The control forces are obtained by solving the quadratic programming problem. The collision avoidance control simulation of the DFP spacecraft has performed while the payload module subjects to sudden disturbance. The simulation results verify the effectiveness of the proposed control strategy and show that the proposed MPC controller exhibits better collision avoidance capability than that of the PD controller.