An efficient system for combined route traversal and collision avoidance

Here we consider the problem of a robot that must follow a previously designated path outdoors. While the nominal path, a series of closely spaced via points, is provided with an assurance that it will lead to the destination, we can’t be guaranteed that it will be obstacle free. We present an efficient system capable of both following the path as well as being perceptive and agile enough to avoid obstacles in its way. We present a system that detects obstacles using laser ranging, as well as a layered system that continuously tracks the path, avoiding obstacles and replanning the route when necessary. The distinction of this system is that compared to the state of the art, it is minimal in sensing and computation while achieving high speeds. In this paper, we present an algorithm that is based on models of obstacle avoidance by humans and show variations of the model to deal with practical considerations. We show how the parameters of this model are automatically learned from observation of human operation and discuss limitations of the model. We then show how these models can be extended by adding online route planning and a formulation that allows for operation at varying speeds. We present experimental results from an autonomous vehicle that has operated several hundred kilometers to validate the methodology.

Robust sliding-mode formation control and collision avoidance via repulsive vector fields for a group of Quad-Rotors

This paper is focused on solving the collision avoidance problem for a group of Quad-Rotors which are affected by external disturbances when they are moving in a horizontal plane by means of Repulsive Vector Fields (RVFs). The RVFs are included in an attractive potential function control strategy, that allows the Quad-Rotors to reach the desired position in a geometric pattern, together with a Continuous Sliding-Mode Control (C-SMC) strategy based on Sliding-Mode Observers (SMOs) that are used to estimate, in a finite-time, the linear and angular velocities, respectively. For this purpose, a parameter, which depends on the distance among the Quad-Rotors and their velocities, is designed in order to scale the RVFs properly. In this sense, the repulsion force will be proportional to the velocity and acceleration of the Quad-Rotor when it detects any obstacle. A RVF not properly scaled may result in a collision or a Quad-Rotor movement far away from its desired position. The combination between the C-SMC strategy, the SMOs, the attractive potential functions and the RVFs robustly solves the formation control and avoidance collision problem under the presence of disturbances. Numerical simulations illustrate the performance of the RVFs when the Quad-Rotors are in risk of collision.     

Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment

In this paper, vision-based autonomous flight with a quadrotor type unmanned aerial vehicle (UAV) is presented. Automatic detection of obstacles and junctions are achieved by the use of optical flow velocities. Variation in the optical flow is used to determine the reference yaw angle. Path to be followed is generated autonomously and the path following process is achieved via a PID controller operating as the low level control scheme. Proposed method is tested in the Google Earth® virtual environment for four different destination points. In each case, autonomous UAV flight is successfully simulated without observing collisions. The results show that the proposed method is a powerful candidate for vision based navigation in an urban environment. Claims are justified with a set of experiments and it is concluded that proper thresholding of the variance of the gradient of optical flow difference have a critical effect on the detectability of roads having different widths.     

Improving collision avoidance for mobile robots in partially known environments: the beam curvature method

This paper presents a new approach to obstacle avoidance for mobile robots in cluttered and unknown or partially unknown environments. The method combines a new directional method, called beam method (BM), to improve the performance of a local obstacle avoidance approach called curvature velocity method (CVM). BM calculates the best one-step heading which is used by CVM to obtain the optimal linear and angular velocities. The resulting combined technique is called beam curvature method (BCM).

Different experiments in populated and dynamic environments have proved to be very successful. The method is able to guide the robot safely and efficiently during long time periods. We present some of these results compared with other methods.     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

A bounded distributed connectivity preserving aggregation strategy with collision avoidance property

This paper presents a bounded connectivity preserving control strategy for the aggregation of multi-agent systems. The problem is investigated for two cases of single-integrator agents and unicycles. Under the proposed control strategy, if two agents are in the connectivity range at some point in time, they will stay connected thereafter. The agents finally aggregate while avoiding collision in such a way that the average of the distances between every pair of neighboring agents is bounded by a pre-specified positive real number, which can be chosen arbitrarily small. The results are developed based on some important characteristics of the positive limit set of the closed-loop system under the proposed control strategy and a fundamental property of convex real functions. The theoretical results are verified by simulation.     

Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem

Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.     

Coordination control of multiple ellipsoidal agents with collision avoidance and limited sensing ranges

This paper contributes a design of cooperative controllers that force N mobile agents with an ellipsoidal shape and a limited sensing range to track desired trajectories and to avoid collision between them. A separation condition for ellipsoidal agents is first derived. Smooth step functions are then introduced. These functions and the separation condition between the ellipsoidal agents are embedded in novel pairwise collision avoidance functions to design coordination controllers. The proposed control design guarantees (1) smooth coordination controllers despite the agents’ limited sensing ranges, (2) no collision between any agents, (3) asymptotical stability of desired equilibrium set, and (4) instability of all other undesired critical sets of the closed loop system.     

Extending obstacle avoidance methods through multiple parameter-space transformations

Obstacle avoidance methods approach the problem of mobile robot autonomous navigation by steering the robot in real-time according to the most recent sensor readings, being suitable to dynamic or unknown environments. However, real-time performance is commonly gained by ignoring the robot shape and some or all of its kinematic restrictions which may lead to poor navigation performance in many practical situations. In this paper we propose a framework where a kinematically constrained and any-shape robot is transformed in real-time into a free-flying point in a new space where well-known obstacle avoidance methods are applicable. Our contribution with this framework is twofold: the definition of generalized space transformations that cover most of the existing transformational approaches, and a reactive navigation system where multiple transformations can be applied concurrently in order to optimize robot motion decisions. As a result, these transformations allow existing obstacle avoidance methods to perform better detection of the surrounding free-space, through “sampling” the space with paths compatible with the robot kinematics. We illustrate how to design these space transformations with some examples from our experience with real robots navigating in indoor, cluttered, and dynamic scenarios. Also, we provide experimental results that demonstrate the advantages of our approach over previous methods when facing similar situations.

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.     

Spacecraft relative rotation tracking without angular velocity measurements

We present a solution to the problem of tracking relative rotation in a leader-follower spacecraft formation using feedback from relative attitude only. The controller incorporates an approximate-differentiation filter to account for the unmeasured angular velocity. We show uniform practical asymptotic stability (UPAS) of the closed-loop system. For simplicity, we assume that the leader is controlled and that we know orbital perturbations; however, this assumption can be easily relaxed to boundedness without degrading the stability property. We also assume that angular velocities of spacecraft relative to an inertial frame are bounded. Simulation results of a leader-follower spacecraft formation using the proposed controller structure are also presented.     

碰撞防护系统分析及铁路应用设计

在分析既有碰撞防护系统的基础上,首次提出适用于轨道交通领域的列车碰撞防护系统概念,设计了列车碰撞系统的功能架构并定义了各子系统的功能,对比分析了列车碰撞防护系统的两种工作模式.作为列车控制系统的安全叠加系统,列车碰撞防护系统可有效提升铁路运输安全性能.     

Proportional navigation-based collision avoidance for UAVs

A collision avoidance algorithm for unmanned aerial vehicles (UAVs) based on the conventional proportional navigation (PN) guidance law is investigated. The proportional navigation guidance law being applied to a wide range of missile guidance problems is tailored to the collision avoidance of UAVs. This can be accomplished by guiding the relative velocity vector of the aircraft to a vector connecting the current aircraft position to the safety boundary of the target aircraft. Stability of the proposed algorithm is also studied using the circle criterion. The stability condition can be established by choosing the navigation coefficient within a certain bound. The guidance law is extended to 3-dimensional maneuver problems. Inherent simplicity and robustness of the PN guidance law provides satisfactory collision avoidance performance with different initial conditions. Recommended by Editorial Board member Sangdeok Park under the direction of Editor Hyun Seok Yang. This research was performed for the Smart UAV Development Program, one of 21st Century Frontier R&D Programs funded by the Ministry of Science and Technology of Korea. Su-Cheol Han received the B.S. degree from Korea Airforce Academy, Korea, in 1997, and the M.S. degree from Korea Advanced Institute of Science and Technology, Korea, in 2005. At present, he is serving as a pilot in Korea Airforce. His research interests are UAV guidance and control, especially collision avoidance. Hyochoong Bang received the B.S. and M.S. degrees in aeronautical engineering from Seoul National University in 1985 and 1987, respectively. He also received the Ph.D. degree in 1992 from Texas A&M University. From 1992 to 1994, he worked as a Research Assistant Professor at the U.S. Naval Postgraduate School (NPS) conducting spacecraft attitude control research. From 1995 to 1999, he worked for Korea Aerospace Research Institute. Since 2001 he has been a Professor at Korea Advanced Institute of Science and Technology. His current research interest include spacecraft attitude control, spacecraft guidance, UAV guidance and control. Chang-Sun Yoo received the B.S. degree from Korea Aerospace University, Korea, in 1987, the M.S. degree from Korea Advanced Institute of Science and Technology, Korea, in 1991 and the Ph.D. degree from Chungnam National University, Korea, in 2003. Since 1991, he has been a Research Engineer in Korea Aerospace Research Institute, Korea. His research interests are flight simulation, flight control system, inertial and GPS navigation.     

Finite-time consensus and collision avoidance control algorithms for multiple AUVs

In this paper, finite-time position consensus and collision avoidance problems are investigated for multi-AUV (autonomous underwater vehicle) systems. First, based on the homogeneous control method, finite-time position consensus algorithms are proposed for both leaderless and leader–follower multi-AUV systems without considering collisions between the AUVs. Specifically, in the leader–follower case, a novel distributed finite-time observer is developed for the followers to estimate the leader’s velocity. Second, by constructing collision avoidance and connectivity maintenance functions, modified consensus algorithms containing corresponding gradient terms are presented for multi-AUV systems of both cases, which guarantee collision avoidance, connectivity maintenance, velocity matching, and consensus boundedness. Simulations demonstrate the effectiveness of the proposed control algorithms.     

A framework and automotive application of collision avoidance decision making

Collision avoidance (CA) systems are applicable for most transportation systems ranging from autonomous robots and vehicles to aircraft, cars and ships. A probabilistic framework is presented for designing and analyzing existing CA algorithms proposed in literature, enabling on-line computation of the risk for faulty intervention and consequence of different actions. The approach is based on Monte Carlo techniques, where sampling-resampling methods are used to convert sensor readings with stochastic errors to a Bayesian risk. The concepts are evaluated using a real-time implementation of an automotive collision mitigation system, and results from one demonstrator vehicle are presented.     

嵌入式船载系统防碰撞预警研究

在分析了各因素对船舶碰撞危险度影响情况后,系统简化了计算,使其在嵌入式系统中可用,且仅对危机情况进行碰撞预警,从而辅助防止船舶碰撞事故的发生。     

Point-to-point and collision avoidance control of the motion of an autonomous bicycle

This work deals with the stabilization and collision avoidance control of a riderless bicycle (see Figure 1). It is assumed here that the bicycle is controlled by a pedalling torque, a directional torque, and by a rotor mounted on the crossbar that generates a tilting torque.Given two points r_A, r_B, and a circular obstacle in the horizontal plane. Also, given a time interval [0, t_f], where 0 < t_f < ∞. It is shown here that by applying a kind of inverse dynamics control, the motion of the bicycle is stabilized while simultaneously controlling its speed and direction in such a manner that the point of contact between the bicycle's rear wheel and the horizontal plane will be able, during [0, t_f], to move from r_A to r_B without hitting the obstacle.     

Spacecraft attitude control using magnetic actuators

The problem of inertial pointing for a spacecraft with magnetic actuators is addressed and an almost global solution to the problem is obtained by means of static attitude and rate feedback. A local solution based on dynamic attitude feedback is also presented. Simulation results demonstrate the practical applicability of the proposed approach.     

Obstacle avoidance control of redundant robots using variants of particle swarm optimization

Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two algorithms are hybrid version of the first two. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside velocity update routine in updating particles' position. The computational results reveal that PSO-W yields better performance in terms of faster convergence and accuracy.