Showing papers on "Collision avoidance published in 2013"

Cooperative Collision Avoidance at Intersections: Algorithms and Experiments

Abstract: In this paper, we leverage vehicle-to-vehicle (V2V) communication technology to implement computationally efficient decentralized algorithms for two-vehicle cooperative collision avoidance at intersections. Our algorithms employ formal control theoretic methods to guarantee a collision-free (safe) system, whereas overrides are only applied when necessary to prevent a crash. Model uncertainty and communication delays are explicitly accounted for by the model and by the state estimation algorithm. The main contribution of this work is to provide an experimental validation of our method on two instrumented vehicles engaged in an intersection collision avoidance scenario in a test track.

Integrated Optimal Formation Control of Multiple Unmanned Aerial Vehicles

Abstract: In this paper, we investigate the formation control of multiple unmanned aerial vehicles (UAVs), specifically unmanned aircraft, in an obstacle-laden environment. The main contribution of this paper is to integrate the formation control, trajectory tracking, and obstacle/collision avoidance into one unified optimal control framework. A nonquadratic avoidance cost is innovatively constructed via an inverse optimal control approach, which leads to an analytical, distributed, and optimal formation control law. The stability and optimality of the closed-loop system are proven. In addition, the proposed optimal control law is dependent only on the information from the local neighbors, rather than all UAVs' information. Simulation of multiple UAVs' formation flying demonstrates the effectiveness of the integrated optimal control design with desired behaviors including formation flying, trajectory tracking, and obstacle/collision avoidance.

Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots

Abstract: In this paper an optimalmethod for distributed collision avoidance among multiple non-holonomic robots is presented in theory and experiments. Non-holonomic optimal reciprocal collision avoidance (NH-ORCA) builds on the concepts introduced in [2], but further guarantees smooth and collision-free motions under non-holonomic constraints. Optimal control inputs and constraints in velocity space are formally derived for the non-holonomic robots. The theoretical results are validated in several collision avoidance experiments with up to fourteen e-puck robots set on collision course. Even in scenarios with very crowded situations, NH-ORCA showed to be collision-free for all times.

UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter

Abstract: Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles

Abstract: We present a simple biologically inspired strategy for the navigation of a unicycle-like robot towards a target while avoiding collisions with moving obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

Systems and methods for reporting characteristics of automatic-driving software

Abstract: A vehicle may include a monitoring unit configured to determine a characteristic of an automatic-driving software program operating on the vehicle. The characteristic may include an identification of the automatic-driving software program, adjustable parameters, internal variables/dynamic decisions of the automatic-driving software program, performance of one or more sensors, whether an automatic collision avoidance maneuver has been triggered, and/or the like. The characteristic may be transmitted to one or more nearby vehicles to improve the ability of manual operators and/or automatic-driving software of the nearby vehicles to predict the maneuvers by the vehicle and/or behavior of the vehicle. Record of the transmission of the characteristic and/or of any acknowledgements of receipt of the characteristic by the nearby vehicles may be logged to a persistent storage device.

A 3D Collision Avoidance Strategy for UAVs in a Non-Cooperative Environment

Abstract: This paper presents a feasible 3D collision avoidance approach for fixed-wing unmanned aerial vehicles (UAVs) The proposed strategy aims to achieve the desired relative bearing in the horizontal plane and relative elevation in the vertical plane so that the host aircraft is able to avoid collision with the intruder aircraft in 3D The host aircraft will follow a desired trajectory in the collision avoidance course and resume the pre-arranged trajectory after collision is avoided The approaching stopping condition is determined for the host aircraft to trigger an evasion maneuver to avoid collision in terms of measured heading A switching controller is designed to achieve the spatial collision avoidance strategy Simulation results demonstrate that the proposed approach can effectively avoid spatial collision, making it suitable for integration into flight control systems of UAVs

Hybrid long-range collision avoidance for crowd simulation

Abstract: Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates.Another issue often sidestepped in existing work is that discrete and continuum collision avoidance algorithms have different regions of applicability. For example, low-density crowds cannot be modeled as a continuum, while high-density crowds can be expensive to model using discrete methods. We formulate a hybrid technique for crowd simulation which can accurately and efficiently simulate crowds at any density with seamless transitions between continuum and discrete representations. Our approach blends results from continuum and discrete algorithms, based on local density and velocity variance. In addition to being robust across a variety of group scenarios, it is also highly efficient, running at interactive rates for thousands of agents on portable systems.

Model predictive coordination of autonomous vehicles crossing intersections

Abstract: This paper presents a decentralized model predictive control approach for the coordination of autonomous vehicles at intersections. A linear quadratic optimal controller is introduced for each vehicle with predefined path, in order to minimize energy as well as to pass intersection smoothly. We guarantee collision avoidance by adding linear constraints to the optimization problem. We apply the so-called soft constraints for the collision avoidance problem to reduce the computation time in order to be able to run the simulations in real-time. In addition, the method can take into account crossing of vehicles in platoons by extending the linear quadratic cost functions.

Visual navigation of a mobile robot with laser-based collision avoidance

Abstract: In this paper, we propose and validate a framework for visual navigation with collision avoidance for a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such a path, the robot is able to avoid obstacles which were not present during teaching, and which are sensed by an on-board range scanner. Our control scheme guarantees that obstacle avoidance and navigation are achieved simultaneously. In fact, in the presence of obstacles, the camera pan angle is actuated to maintain scene visibility while the robot circumnavigates the obstacle. The risk of collision and the eventual avoiding behaviour are determined using a tentacle-based approach. The framework can also deal with unavoidable obstacles, which make the robot decelerate and eventually stop. Simulated and real experiments show that with our method, the vehicle can navigate along a visual path while avoiding collisions.

Impact of V2X privacy strategies on Intersection Collision Avoidance systems

Abstract: User privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. In addition to their ability to protect privacy, it is important to ensure that these privacy strategies do not hinder the safety applications which rely on vehicular communications. This paper addresses this concern and presents an experimental analysis of the impact of privacy strategies on Intersection Collision Avoidance (ICA) systems. We simulate traffic scenarios at a road intersection and compare the ability of a collision avoidance system to avoid collisions for different pseudonym change schemes. The privacy level is analyzed, as well as the influence of the duration of the silent period on the safety performance of the ICA system. The results highlight the need to jointly design safety applications and privacy strategies.

Reciprocal collision avoidance for robots with linear dynamics using LQR-Obstacles

Abstract: In this paper we present a formal approach to reciprocal collision avoidance for multiple mobile robots sharing a common 2-D or 3-D workspace whose dynamics are subject to linear differential constraints. Our approach defines a protocol for robots to select their control input independently (i.e. without coordination with other robots) while guaranteeing collision-free motion for all robots, assuming the robots can perfectly observe each other's state. To this end, we extend the concept of LQR-Obstacles (which is a generalization of Velocity Obstacles to robots with dynamics for collision avoidance among static obstacles) for reciprocal collision avoidance among multiple robots. We implemented and tested our approach in 3-D simulation environments for reciprocal collision avoidance of quadrotor helicopters, which have complex dynamics in 16-D state spaces. Our results show that our approach enables collision avoidance among over a hundred quadrotors in tight workspaces at real-time computation rates.

Vision-guided active collision avoidance for human-robot collaborations

Abstract: This paper reports a novel methodology of real-time active collision avoidance in an augmented environment, where virtual 3D models of robots and real camera images of operators are used for monitoring and collision detection. A prototype system is developed and linked to robot controllers for adaptive robot control, with zero robot programming for end users. According to the result of collision detection, the system can alert an operator, stop a robot, or modify the robot’s trajectory away from an approaching operator. Through a case study, it shows that this method can be applied to real-world applications such as human-robot collaborative assembly to safeguard human operators.

Control, navigation and collision avoidance for an unmanned aerial vehicle

Abstract: This article reports the development of an unmanned aerial vehicle capable of attitude estimation and stabilization through the implementation of a nonlinear complementary filter and proportional-integral rate controllers. Four infra-red sensors and an ultrasonic sensor are integrated with the main platform for the collision avoidance schemes and for altitude control, respectively. Critical mission capabilities for the vehicle such as altitude hold and collision avoidance are developed. An outdoor navigation scheme and collision avoidance algorithms are also proposed to enhance the vehicle autonomy. Experimental results have shown that the implemented attitude and altitude controllers are effective and the platform is capable of navigating autonomously with user-defined waypoints. The collision avoidance algorithms allow the platform to avoid obstacles, both reactively and in the midst of navigation routines.

Establishing Obstacle and Collision Free Communication Relay for UAVs with Artificial Potential Fields

Abstract: In this paper, Unmanned Aerial Vehicles are used for establishing an airborne communication relay chain to extend the communication range or to obtain a channel between two far points which are outside the single UAV communication range. Positions of the UAVs in the chain are detected by the vehicles autonomously and while establishing the suitable formation, collision avoidance between vehicles and other geographical obstacles are considered by using artificial potential fields. Especially to provide reliable continues communication between vehicles as uninterrupted channels, positions of the UAVs which are providing line of sight, calculated automatically by tuning artificial potential field parameters dynamically. The success of this novel approach is expressed by simulation studies in Matlab environment and the simulation results are validated using NS2 simulator.

A cluster based architecture for intersection collision avoidance using heterogeneous networks

Abstract: With the popularity of wireless devices, the possibility of implementing vehicular safety applications has been studied for years in the context of vehicular ad-hoc networks. Dedicated Short Range Communication (DSRC) is designed to serve the needs of vehicular safety applications. However, DSRC does not offer good enough coverage and range around intersections in urban areas for certain applications such as intersection collision avoidance. Considering these drawbacks, LTE, an advanced cellular communication technology, is proposed as an alternative to DSRC. One problem is LTE bandwidth capability to support regularly transmitted cooperative awareness messages. In this paper, we propose a cluster based architecture using both Wi-Fi and LTE channels to accomplish this task. In our architecture, Wi-Fi peer to peer channels are used for cluster formation while LTE channels are used for transmitting Cooperative Awareness Messages (CAMs). A clustering algorithm specifically designed for intersection collision avoidance service is proposed in this paper. In addition, a channel allocation algorithm is applied to reduce the interference of Wi-Fi channels between different clusters. Simulations show that CAM traffic can be efficiently supported in this architecture.

A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

Abstract: This paper is concerned with the problem of decision-making in systems that assist drivers in avoiding collisions. An important aspect of these systems is not only assisting the driver when needed but also not disturbing the driver with unnecessary interventions. Aimed at improving both of these properties, a probabilistic framework is presented for jointly evaluating the driver acceptance of an intervention and the necessity thereof to automatically avoid a collision. The intervention acceptance is modeled as high if it estimated that the driver judges the situation as critical, based on the driver's observations and predictions of the traffic situation. One advantage with the proposed framework is that interventions can be initiated at an earlier stage when the estimated driver acceptance is high. Using a simplified driver model, the framework is applied to a few different types of collision scenarios. The results show that the framework has appealing properties, both with respect to increasing the system benefit and to decreasing the risk of unnecessary interventions.

Model Predictive Formation Control Using Branch-and-Bound Compatible With Collision Avoidance Problems

Abstract: This paper presents a model predictive control (MPC) approach for multivehicle formation taking into account collision avoidance and velocity limitation with reduced computational burden. The first part of the paper constructs a formation control law using feedback linearization with MPC in order to reduce the optimal control problem to a mixed-integer quadratic programming problem for a group of unicycles. The second part constructs a new branch-and-bound (B& B) -based algorithm for collision-avoidance problems. Numerical examples and experiments show that the proposed method significantly reduces computation time.

Vehicle-based automatic traffic conflict and collision avoidance

Abstract: Systems and methods for providing vehicle-centric collision avoidance are disclosed. In one embodiment, a method includes determining a first flight trajectory for a first aircraft. The method also includes determining a second flight trajectory for a second aircraft. A distance between the first aircraft and the second aircraft at a first closest point of approach (CPA) is predicted. The predicted closest point of approach is then compared to a separation perimeter layer. The separation perimeter layer is configured to provide a minimum separation distance from the first aircraft to the second aircraft. When the predicted closest point of approach breaches the separation perimeter, the first flight trajectory is altered to provide collision avoidance.

Context-aware collision devices and collision avoidance system comprising the same

Abstract: Personnel and vehicle collision avoidance devices configured to be used in collision avoidance systems are disclosed. The collision avoidance devices are configured to be aware of the context (e.g. position, location, state, status, etc.) in which the person or vehicle is. This awareness allows the devices to avoid transmitting non-hazardous proximity warnings when the context does not warrant the transmission of proximity warnings, and to transmit special critical proximity warnings when the context warrants the transmission of such proximity warnings. To detect the context, the devices comprise one or more context-awareness mechanisms (e.g. user input interfaces, sensors, infra-red receivers, etc.), each of which being capable of detecting one or more particular contexts. A collision avoidance system comprising these personnel and vehicle collision avoidance devices is also presented.

Wireless Vehicular Networks for Car Collision Avoidance

Abstract: Wireless Vehicular Networks for Car Collision Avoidance focuses on the development of the ITS (Intelligent Transportation Systems) in order to minimize vehicular accidents. The book presents and analyses a range of concrete accident scenarios while examining the causes of vehicular collision and proposing countermeasures based on wireless vehicular networks. The book also describes the vehicular network standards and quality of service mechanisms focusing on improving critical dissemination of safety information. With recommendations on techniques and protocols to consider when improving road safety policies in order to minimize crashes and collision risks.

Formal verification of distributed aircraft controllers

Abstract: As airspace becomes ever more crowded, air traffic management must reduce both space and time between aircraft to increase throughput, making on-board collision avoidance systems ever more important. These safety-critical systems must be extremely reliable, and as such, many resources are invested into ensuring that the protocols they implement are accurate. Still, it is challenging to guarantee that such a controller works properly under every circumstance. In tough scenarios where a large number of aircraft must execute a collision avoidance maneuver, a human pilot under stress is not necessarily able to understand the complexity of the distributed system and may not take the right course, especially if actions must be taken quickly. We consider a class of distributed collision avoidance controllers designed to work even in environments with arbitrarily many aircraft or UAVs. We prove that the controllers never allow the aircraft to get too close to one another, even when new planes approach an in-progress avoidance maneuver that the new plane may not be aware of. Because these safety guarantees always hold, the aircraft are protected against unexpected emergent behavior which simulation and testing may miss. This is an important step in formally verified, flyable, and distributed air traffic control.

Vehicular Trajectory Optimization for Cooperative Collision Avoidance at High Speeds

Abstract: Traffic safety is a key aspect in new-generation intelligent transportation systems. Among other areas, an active field of research is cooperative collision avoidance, where vehicles cooperatively calculate trajectories under tight time constraints to avoid colliding under specific road-traffic domains (overtaking, intersections, etc.). In this paper, we particularly analyze the problem of collision avoidance in scenarios in which high-speed vehicles need to generate evasive maneuvers within very short time intervals to avoid or at least mitigate a hypothetical (multiple) collision. We pose this as a multiobjective optimization problem and simplify it by considering only lateral motion for the optimization process, thus having to solve a 1-D trajectory generation problem. The routes of vehicles are optimized according to a weighted aggregation functional that: 1) maximizes the lateral distances between vehicle-vehicle and vehicle-obstacle pairs at the time of overcoming the obstacles; 2) minimizes the lateral speeds at the end of the path; and 3) minimizes the instantaneous lateral acceleration (inertia) along the maneuver. In addition, we compute trajectories by following an optimization strategy that divides the problem into a set of independent subproblems, which are optimized in parallel by using a gradient-descent-based methodology. From this set of solutions, the most suitable option, according to our selected criteria, is chosen. Results show the utility of our approach and its flexibility to compute evasive trajectories adapted to different requirements. Additionally, a simulation of the mechanical response of the vehicles during the evasive maneuvers is conducted.

Multimodal obstacle detection and collision avoidance for micro aerial vehicles

Abstract: Reliably perceiving obstacles and avoiding collisions is key for the fully autonomous application of micro aerial vehicles (MAVs), Limiting factors for increasing autonomy and complexity of MAVs are limited onboard sensing and limited onboard processing power. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception. We developed a lightweight 3D laser scanner and visual obstacle detection using wide-angle stereo cameras. Detected obstacles are aggregated in egocentric grid maps. We implemented a fast reactive collision avoidance approach for safe operation in the vicinity of structures like buildings or vegetation.

Human–robot collision avoidance using a modified social force model with body pose and face orientation

Abstract: The ability of robots to understand human characteristics and make themselves socially accepted by humans are important issues if smooth collision avoidance between humans and robots is to be achieved. When discussing smooth collision avoidance, robot should understand not only physical components such as human position, but also social components such as body pose, face orientation and proxemics (personal space during motion). We integrated these components in a modified social force model (MSFM) which allows robots to predict human motion and perform smooth collision avoidance. In the modified model, short-term intended direction is described by body pose, and a supplementary force related face orientation is added for intention estimation. Face orientation is also the best indication of the direction of personal space during motion, which was verified in preliminary experiments. Our approach was implemented and tested on a real humanoid robot in a situation in which a human is confronted with the robot in an indoor environment. Experimental results showed that better human motion tracking was achieved with body pose and face orientation tracking. Being provided with the face orientation as an indication of the intended direction, and observing the laws of proxemics in a human-like manner, the robot was able to perform avoidance motions that were more human-like when compared to the original social force model (SFM) in a face-to-face confrontation.