Collision Avoidance Scenarios Research Articles

Analytical solution of satellite formation impulsive reconfiguration considering passive safety constraints

To handle the potential collision hazards caused by utilizing original fuel optimal impulsive reconfiguration approach, an analytical impulsive satellite formation reconfiguration method in terms of relative eccentricity and inclination vectors (E/I vectors) considering passive safety constraints is proposed. Firstly, the mechanism why original fuel optimal impulsive reconfiguration approach based on relative E/I vectors may cause unsatisfied collision avoidance constraints is analyzed. The unsatisfied scenarios are divided into two cases, namely, the single impulse is too large case and the middle relative E vector is in passive unsafety region case. Secondly, with respect to the unsatisfied collision avoidance scenarios, corresponding analytical impulsive reconfiguration strategies with passive safety constraints are proposed. Finally, the results of the numerical simulations show that the proposed reconfiguration methods can handle the passive safety constraints.

Online Synthesis for Runtime Enforcement of Safety in Multiagent Systems

We study the problem of enforcing safety in multiagent systems at runtime by modifying the system behavior if a potential safety violation is detected. Traditional runtime enforcement methods that solve a reactive synthesis problem at design time have two significant drawbacks. First, these techniques do not scale as one has to take into account all possible behaviors from every agent, and this is computationally prohibitive. Second, these approaches require every agent to know the state of every other agent. We address these limitations through a new approach where online modifications to behavior are synthesized onboard every agent. There is an enforcer onboard every agent, which can modify the behavior of only the corresponding agent. In this approach, which is naturally decentralized, the enforcer on every agent has two components: 1) a pathfinder that corrects the behavior of the agent and 2) an ordering mechanism that dynamically modifies the priority of the agent. The current priority of an agent determines if the enforcer uses the pathfinder to modify the behavior of the agent. We derive an upper bound on the maximum deviation for any agent from its original behavior; that is, all agents make progress. We prove that the worst case synthesis time is quadratic in the number of agents at runtime as opposed to exponential at design time for the existing methods that rely on design-time computation merely. Additionally, we prove the completeness of the technique under some mild assumptions; that is, if the agents can progress safely, then enforcers will find this behavior. We test the technique in collision avoidance scenarios. For 50 agents in a 50 × 50 grid world modeling the common workspace for the agents, the online synthesis requires only a few seconds per agent whenever a potential collision is detected. In contrast, the centralized design-time synthesis of shields for a similar setting is intractable beyond four agents in a 5 × 5 grid world.

Driving and steering collision avoidance system of autonomous vehicle with model predictive control based on non-convex optimization

A planar motion control system is proposed for autonomous vehicles not only to follow the lanes, but also to avoid collisions by braking, accelerating, and steering. The supervisor is designed first to determine the desired speed and the risk of the maneuvering due to road boundaries and obstacles. In order to allow lane changes on multi-lane roads, the model predictive controller is formulated based on the probabilistic non-convex optimization. The micro-genetic algorithm is applied to calculate the target speed and target steering angle in real time. A software-in-the-loop unit is constructed with the Rapid Control Prototyping device in the vehicle communication environment. The performance of the proposed system is verified for various collision avoidance scenarios and the simulation results demonstrate the safe and effective driving performance of autonomous vehicles with no collision on multi-lane road.

Control barrier functions for stochastic systems

Control Barrier Functions (CBFs) aim to ensure safety by constraining the control input at each time step so that the system state remains within a desired safe region. This paper presents a framework for CBFs in stochastic systems in the presence of Gaussian process and measurement noise. We first consider the case where the system state is known at each time step, and present reciprocal and zero CBF constructions that guarantee safety with probability 1. We extend our results to high relative degree systems and present both general constructions and the special case of linear dynamics and affine safety constraints. We then develop CBFs for incomplete state information environments, in which the state must be estimated using sensors that are corrupted by Gaussian noise. We prove that our proposed CBF ensures safety with probability 1 when the state estimate is within a given bound of the true state, which can be achieved using an Extended Kalman Filter when the system is linear or the process and measurement noise are sufficiently small. We propose control policies that combine these CBFs with Control Lyapunov Functions in order to jointly ensure safety and stochastic stability. Our results are validated via numerical study on a multi-agent collision avoidance scenario.

RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch

Successful robotic operation stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally robust control, which plans locally optimal feedback policies against a worst-case distribution within a given KL divergence bound from a Gaussian distribution. Leveraging mathematical equivalence between distributionally robust control and risk-sensitive optimal control, our framework also provides an algorithm to dynamically adjust the risk-sensitivity level online for risk-sensitive control. The benefits of the distributional robustness as well as the automatic risk-sensitivity adjustment are demonstrated in a dynamic collision avoidance scenario where the predictive distribution of human motion is erroneous.

Visible Light Communication Based Vehicle Localization for Collision Avoidance and Platooning

Collision avoidance and platooning applications require vehicle localization at cm-level accuracy and at least 50 Hz rate for full autonomy. The RADAR/LIDAR and camera based methods currently used for vehicle localization do not satisfy these requirements, necessitating complementary technologies. Visible light positioning (VLP) is a highly suitable complementary technology due to its high accuracy and high rate, exploiting the line-of-sight propagation feature of the visible light communication (VLC) signals from LED head/tail lights. However, existing vehicular VLP algorithms impose restrictive requirements, e.g., use of high-bandwidth circuits, road-side lights and certain VLC modulation strategies, and work for limited relative vehicle orientations, thus, are not feasible for general use. This paper proposes a VLC-based vehicle localization method that eliminates these restrictive requirements by a novel VLC receiver design and associated vehicular VLP algorithm. The VLC receiver, named QRX, is low-cost/size, and enables high-rate VLC and high-accuracy angle-of-arrival (AoA) measurement, simultaneously, via the usage of a quadrant photodiode. The VLP algorithm estimates the positions of two head/tail light VLC transmitters (TX) on a neighbouring vehicle by using AoA measurements from two QRXs for localization. The algorithm is theoretically analyzed by deriving its Cramer-Rao lower bound on positioning accuracy, and simulated localization performance is evaluated under realistic platooning and collision avoidance scenarios. Results demonstrate that the proposed method performs at cm-level accuracy and up to 250 Hz rate within a 10 m range under realistic harsh road and channel conditions, demonstrating its eligibility for collision avoidance and safe platooning.

Graphical composite modeling and simulation for multi-aircraft collision avoidance

Modeling and simulation for multi-aircraft collision avoidance to understand the mechanistic behavior is an important activity. Building models using general programming language typically requires specialist knowledge, and this limits the spread of modeling and simulation approach among multi-aircraft collision avoidance scenario. Thus, a software environment is needed to support convenient development of models by assembling components, when analysis demands changes. In this work, the graphical composite modeling and simulation software (GMAS extended) for multi-aircraft collision avoidance is introduced, with the basic graphical components and a graphical assembly editor. We define the serial and parallel execution semantics of GMASE-based model and then introduce the high-level graphical modeling interface, the low-level runtime engine of GMAS, and the simulation-based decision tree, which transforms a complex decision-making process into a collection of simpler decisions of finding the no collision or optimal sequence from some initial state to the goal state. To validate its efficiency and practicability, a three-aircraft collision avoidance model with TCAS operations is built on GMAS, which shows that using GMAS increases reusability and hiding complexity in graphical programming by splitting complex behavior into data flow and function components. The experimental result proves that GMAS not only provides a better representation for multi-aircraft collision avoidance, but also a useful approach for analyzing the potential collision occurrences.

On the Use of the Hybrid Causal Logic Methodology in Ship Collision Risk Assessment

A ship collision accident is one of the most dangerous and common types of maritime accidents. Traditional probabilistic risk assessment (PRA) of ship collision accidents is a methodology that can be adopted to ensure maritime safety. Nevertheless, a need for better approaches to model human behavior, such as risk identification, communication, and decision-making, has been identified. Such advanced PRA methods require a more explicit way of taking human factors into consideration than the traditional risk assessment methods. Hybrid causal logic (HCL) is an advanced PRA method due to its unique three-level framework that includes event sequence diagrams, fault trees, and Bayesian networks, which makes it suitable for modeling human behavior that is important to ship collision accidents. This paper discusses the applicability of the HCL methodology for the ship collision accident. Firstly, the event sequences of typical ship collision accidents are summarized based on the study of 50 accident investigation reports. Then, fault trees for mechanical failure events and the Bayesian networks for human error events are constructed to analyze the events in a structured way at a more detailed level. Finally, the three main end-state types of ship collision avoidance scenario have been quantified. The result of the probability of a ship collision accident is verified by estimating the annual frequency of collision accidents in the Singapore Strait. Comparing with the historical data, the estimation results are quite near to the real case. By taking advantage of the HCL methodology, the modeling of ship collision scenarios can be carried out at a deep logical level. At the same time, it is possible to combine a detailed analysis of various primary events with a comprehensive analysis at the system level.

Debris collision avoidance by means of attitude control-in-flight demonstration with TET-1

Collision avoidance is more and more of importance due to the growing amount of space debris posing a threat not only on satellites in orbit but also on upcoming missions. To avoid a collision between space debris and functioning satellites or even between two functioning satellites collision avoidance manoeuvres can be induced if one of the satellites has a working propulsion system. An alternative method of collision avoidance operations is presented in this study. By changing the satellites attitude, it is possible to obtain an increase or decrease in the semi-major axis in relation to its nominal orbital decay. The change in the semi major axis together with the time until closest approach provokes a change in the relative geometry of the object orbits resulting in a decreasing collision risk. This method enables collision avoidance manoeuvres for satellites in Low Earth Orbits featuring active attitude control and drag-susceptible geometries. Additionally, the probability of a collision can be reduced by changing the satellite attitude so that the minimum effective area is perpendicular to the relative velocity vector at the time of closest approach. Both methods can be applied to satellites without propulsion, which otherwise would be defenceless. A test run for verification of collision avoidance by means of drag-minimization was performed in June 2018 with the Technology Experiment Carrier (TET-1) satellite of the FireBIRD constellation. The experiment planning and results are presented along with the initial flight dynamics analysis and representative examples for collision avoidance scenarios.

Robust control of connected vehicles via V2V communication

AbstractThis paper introduces a robust controller for connected vehicles which are communicating through dedicated short‐range communication (DSRC) protocol to avoid collision and improve safety. By a comprehensive analysis on both longitudinal and lateral dynamics of the vehicles, the controller covers a wide range of collision avoidance scenarios, such as: Intersection Movement Assistant (IMA), Automatic Emergency Brake (AEB) and Lane Change Warning (LCW). By implementing an advanced Sliding Mode Controller (SMC), the effect of uncertainties will be mitigated for a variety of path plans. It turns out that the proposed control scheme can robustly improve the safety. Finally, we optimize the performance of vehicles in tracking the paths by an MPC controller.

Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot

In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration with motion control is an unexplored topic. In our work, we propose to utilize the LGMD neural network for visual interception detection with a central pattern generator (CPG) for locomotion control of a hexapod walking robot that are combined in the controller based on the long short-term memory (LSTM) recurrent neural network. Moreover, we propose self-supervised learning of the integrated controller to autonomously find a suitable setting of the system using a realistic robotic simulator. Thus, individual neural networks are trained in a simulation to enhance the performance of the controller that is then experimentally verified with a real hexapod walking robot in both collision and interception avoidance scenario and navigation in a cluttered environment.

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

Case Study: Networked Control for Optimal Maneuvering of Autonomous Vessels

Abstract This paper presents the development of a networked control system for maritime autonomous shipping within the joint project GALILEOnautic 2 (grant 50NA1808). The project aims for autonomous navigation and optimal maneuvering of cooperative vessels in a harbor environment. In this publication, the networked vehicles dynamically estimate position, velocity, attitude and time using a navigation filter. The estimated states are used by an optimization algorithm which calculates optimal trajectories for each active participant. For a secure and efficient data exchange within the system, long term evolution (LTE) mobile communication together with virtual private network (VPN) are used. To compensate for mobile communication latency and time delay by data processing, data synchronization is realized among system components with the help of global navigation satellite system (GNSS). Feedback trajectory control is established to compensate the model errors. Besides active network participants, other vehicles are considered as well by means of the automatic identification system (AIS). Due to high costs related to testing with real vessels, real-world validation is performed with a rapid control prototyping (RCP) platform for research and education developed at RWTH Aachen University, the so called IRT-Buggy. Collision avoidance scenarios in a harbor-like structure defined by geodetic coordinates are evaluated. Results show that the designed networked control system accomplishes the autonomous navigation and maneuvering task as required.

A Markov Decision Process for the Interaction between Autonomous Collision Avoidance and Delayed Pilot Commands

Abstract We propose a Markov Decision Process (MDP) approach to optimizing decision making of an Unmanned Aerial Vehicle (UAV) in the presence of communication latency for collision avoidance scenarios. The UAV can be controlled by a remote human pilot or an onboard collision avoidance system (CAS). Due to communication latency, the pilot’s commands may be delayed for several seconds. If the UAV encounters a dynamic intruder, it has to make a decision on whether it should evade immediately by following the safe path generated from the CAS, or wait for the arrival of the pilot’s commands. The proposed MDP will make predictions on the state change in such a scenario. By solving the MDP with the value iteration method, sequential decisions on whether to wait can be made and an optimal waiting strategy can be obtained, which can be used to improve the human pilot’s user experience while ensuring safety.

A motivational driver steering model: Task difficulty homeostasis from control theory perspective

A general and psychologically plausible collision avoidance driver model can improve transportation safety significantly. Most computational driver models found in the literature have used control theory methods only, and they are not established based on psychological theories. In this paper, a unified approach is presented based on concepts taken from psychology and control theory. The “task difficulty homeostasis theory”, a prominent motivational theory, is combined with the “Lyapunov stability method” in control theory to present a general and psychologically plausible model. This approach is used to model driver steering behavior for collision avoidance. The performance of this model is measured by simulation of two collision avoidance scenarios at a wide range of speeds from 20 km/h to 170 km/h. The model is validated by experiments on a driving simulator. The results demonstrate that the model follows human behavior accurately with a mean error of 7%.

Cognitive Human-Machine Interfaces and Interactions for Unmanned Aircraft

This paper presents the concept of Cognitive Human-Machine Interfaces and Interactions (CHMI2) for Unmanned Aircraft System (UAS) Ground Control Stations (GCS). CHMI2 represents a new approach to aviation human factors engineering that introduces adaptive functionalities in the design of operators' command, control and display functions. A CHMI2 system assesses human cognitive states based on measurement of key psycho-physiological observables. The cognitive states are used to predict and enhance operator performance in the accomplishment of aviation tasks, with the objective of improving the efficiency and effectiveness of the overall human-machine teaming. The CHMI2 system presented in this paper employs a four-layer architecture comprising sensing, extraction, classification and adaptation functionalities. An overview of each layer is provided along with the layer's metrics, algorithms and functions. Two relevant case studies are presented to illustrate the interactions between the different layers, and the conceptual design of the associated display formats is described. The results indicate that specific eye tracking variables provide discrimination between different modes of control. Furthermore, results indicate that the higher levels of automation supported by the CHM 2 are beneficial in Separation Assurance and Collision Avoidance (SA&CA) scenarios involving low-detectability obstacles and stringent time constraints to implement recovery manoeuvres. These preliminary results highlight that the introduction of CHMI2 functionalities in future UAS can significantly reduce reaction time and enhance operational effectiveness of unmanned aircraft response to collision and loss of separation events, as well as improve the overall safety and efficiency of operations.

Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion

Globally, safety has become an increasingly important issue in the automotive industry. In an attempt to reduce traffic fatalities, UNECE launched a new EU Road Safety Program which aims to decrease the number of road deaths by half by 2020. AEB (Autonomous Emergency Braking) is a very effective active safety system intended to reduce fatalities. This study involves the design of a multi-sensor data fusion strategy and decision-making algorithm for AEB pedestrian. Possible collision avoidance scenarios according to the EuroNCAP protocol are analyzed and a robust pedestrian tracking strategy is proposed. The performance of the AEB system is enhanced by using a braking model to predict the collision avoidance time and by designing the system activation zone according to the relative speed and possible distance required to stop for pedestrians. The AEB activation threshold requires careful consideration. The test results confirm the advantages of the proposed algorithm, the performance of which is described in this paper.

Augmented Invariance Control for Impedance-controlled Robots with Safety Margins

Various robotic applications require enforcing constraints, to achieve task performance or to hinder the robot from causing danger. Especially in human-robot-interaction, collision avoidance and velocity limits are crucial for safety A promising approach to enforce adherence to safety margins is invariance control. Considering the system dynamics, it corrects a nominal control based on a switching policy. To overcome the resulting lack of smoothness in terms of control inputs and states, a control scheme is developed that adds dynamics to the input and thereby augments the system. In previous work it is shown that the closed loop system is stable in combination with an exponentially stabilizing nominal control. However, this could not be shown for impedance controlled robots. Therefore, in this paper, an augmented invariance control for robotic applications is developed and proven to be uniformly asymptotically stable with an impedance-type nominal control. The proposed control law is validated in experiments on a KUKA LWR4+ in a collision avoidance scenario demonstrating stable behaviour with improved smoothness and chattering characteristics while enforcing the imposed hard constraints.

Broadcast transponders for low flying unmanned aerial vehicles

Abstract Present day unmanned aerial vehicle (UAV) technology in urban mobility applications is severely limited in use due to lack of reliable collision avoidance mechanisms. There are numerous applications for UAVs in urban environments, including information awareness, urban management, on-demand goods deliveries, and even parking assist, but present day legal regulations preclude their use over safety concerns, requiring presence of a pilot and restricting UAV use in urban areas. Indeed, possibility of midair collision is a significant concern in a densely populated urban area, and will only get worse as UAV use becomes widespread. Collision avoidance cannot be done effectively using onboard sensors. A transponder system like automatic dependent surveillance-broadcast (ADS-B) could be used to augment airspace awareness of UAVs, greatly reducing collision risks. SPH Engineering has successfully integrated such transponders with two of the most popular consumer class autopilots. In each case a device by Sagetech was used, the smallest commercially available transponder to date. This is a major step towards commercial UAV integration into regulated airspace. Unfortunately, ADS-B is ill suited for small low flying UAVs that are navigating unregulated airspace. While weight and power are the more obvious limitations, the use of ADS-B also requires International Civil Aviation Organization (ICAO) certification, which is both expensive and lengthy. Furthermore, at close range receiver was overwhelmed by transmit power. In a collision avoidance scenario for delivery drones short range is exactly where reliable reception is most needed. This article presents an approach, which is used to design a collision avoidance system in accordance with safety regulations of manned aviation that would be specific to small, commercial UAVs operating in unregulated airspace.

A computationally efficient safety assessment for collaborative robotics applications

Safety during interaction with unstructured and dynamic environments is now a well established requirement for complex robotic systems. A wide variety of approaches focus on the introduction of safety evaluation methods in order to shape a consequent safety-oriented control strategy, able to reactively prevent collisions between the robot and potential obstacles, including a human being. This paper presents a new safety assessment, named kinetostatic safety field, that captures the risk in the vicinity of an arbitrary rigid body “source of danger” (e.g. an obstacle, a human body part or a robot link) moving in R3. The safety field depends on the position and velocity of the body but it is also influenced by its real shape and size, exploiting its triangular mesh. The introduction of a body-fixed reference frame in the definition of the field provides closed form computability and an effective computation time reduction, that allows for real-time applications. In particular, intensive computations, connected to the specific body geometry, can be performed only once and off-line, ensuring a fast and constant on-line computation time, independently of the number of mesh elements. Furthermore, we combine the safety field concept with a safety-oriented reactive control strategy for redundant manipulators. Our approach allows to enhance safety in several real-time collision avoidance scenarios, including collision avoidance with potential obstacles, self-collision avoidance and safe human–robot coexistence. The proposed control strategy is validated through experiments performed on an ABB FRIDA dual arm robot.