非结构环境下移动机器人的运动规划

在给出了非结构环境下移动障碍物优先级定义的前提下,提出了移动机器人在含有多个障碍物且障碍物运动具有不确定性的情况下的实时避障运动规划策略.同时给出了障碍物的运动具有不确定性时预测k个采样周期后确定障碍物位置的表达式.仿真结果表明此避障策略是行之有效的,且具有很强的适用性和实际应用价 值.     

Guidance-Based On-Line Robot Motion Planning for the Interception of Mobile Targets in Dynamic Environments

This paper presents a novel method for the interception of moving targets in the presence of obstacles. The proposed method provides simultaneous positional interception and velocity matching of the target moving in a dynamic environment with static and/or mobile obstacles. An acceleration command for the autonomous robot (i.e., interceptor) is first obtained from a rendezvous-guidance technique that takes into account the kinematic and dynamic limitations of the interceptor, but not the motion of the obstacles. This command is subsequently augmented, though only when necessary, in order to avoid those obstacles that are about to interfere with the time-optimal motion of the interceptor. The augmenter acceleration command is obtained in our work through a modified cell-decomposition method. Extensive simulation and experimental results have clearly demonstrated the efficiency of the proposed interception method, tangibly better than other existing obstacle-avoidance methods.     

Velocity planning for a mobile robot to track a moving target — a potential field approach

Potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. The path planning result is normally the direction of the robot motion. In this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. The robot’s planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. The implementation factors such as maximum linear and angular speed of the robot are also considered. The proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. Simulation studies are provided to verify the effectiveness of the proposed approach.     

Dynamic design,numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important ‘bridge’ theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and window-shaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.     

DYNAMIC ENVIRONMENT ROBOT PATH PLANNING USING HIERARCHICAL EVOLUTIONARY ALGORITHMS

The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.     

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.     

一种多移动机器人避障的改进算法

为了使多机器人在有障碍物的环境中可靠地运行,针对多机器人的避障问题,融合沿墙行为的避障模式,构造出一类具有自适应特性l-ψ闭环控制律下的多机器人避障算法,以作为基于行为的控制策略的有益补充。仿真结果表明,该算法可以成功地解决机器人因融合参数不当而形成的避障"死锁"问题,使多机器人在有障碍物的环境下,在障碍物区能够顺利地通过障碍物,在离开障碍物后,快速恢复至稳定。     

Obstacle avoidance for redundant robots using configuration control

The article presents a new and simple solution to the obstacle avoidance problem for redundant robots. In the proposed approach, called configuration control, the redundancy is utilized to configure the robot so as to satisfy a set of kinematic inequality constraints representing obstacle avoidance, while the end-effector is tracking a desired trajectory. The robot control scheme is very simple, and uses on-line adaptation to eliminate the need for the complex dynamic model and parameter values of the robot. Several simulation results for a four-link planar robot are presented to illustrate the versatility of the approach. These include reaching around a stationary obstacle, simultaneous avoidance of two obstacles, robot reconfiguration to avoid a moving obstacle, and avoidance of rectangular obstacles. The simplicity and computational efficiency of the proposed scheme allows on-line implementation with a high sampling rate for real-time obstacle avoidance in a dynamically varying environment.     

Omnidirectional vision scan matching for robot localization in dynamic environments

The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.     

基于行为的实时路径规划

针对室内移动机器人导航要求,开发了以二维激光雷达作为探测环境的传感器,基于4个反应式行为,设计了一种简单的实时路径规划算法.避障行为使机器人穿过狭小通道,或者在某些障碍物环境下绕出狭窄区域;接近行为使机器人顺着障碍物前进直到开阔地带;搜寻行为使机器人不断朝向目标运动;线性行为使机器人到达目标点.机器人表现出很强的路径寻找能力,并且不需要定位信息.仿真实验表明该算法速度快,实时性好,路径平滑无震荡,实现了有效避障.     

A Stable Switched-System Approach to Collision-Free Wheeled Mobile Robot Navigation

This paper presents a novel switched-system approach for obstacle avoidance by mobile robots. This approach does not suffer from common drawbacks of existing methods, such as needing prior knowledge of obstacles, or local minima or chattering in control laws. We define an attractive and an avoidance vector in obstacle-free and obstacle-avoidance regions, respectively. Next, we define an unified velocity vector, which represents either the attractive vector or the avoidance vector, and drives the robot away from the obstacle and ultimately towards the goal. The avoidance vector differs from the repulsive vector commonly used in potential field approaches, rather it is defined always perpendicular to such a repulsive vector and projects positively onto the attractive vector. The unified velocity vector enables the use of a common Lyapunov function in analyzing the stability of the system under arbitrary switching. Novel switching rules are proposed for obstacles that can be well bounded by a circle in the local subset of SE(2). To better handle large, non-circular obstacles, a separate switching signal is proposed. Through the choice of switching rule, we investigate the chattering problem that can hinder some switching controllers. We present two control laws, one with bounded inputs and one with no bounds on inputs. We prove both control schemes are asymptotically stable and guide the robot to the goal while avoiding obstacles. To verify the effectiveness of the proposed approach, as well as compare the control laws and switching rules, several simulations and experiments have been conducted.     

在障碍时变工作空间中机器人无碰撞平滑路径规划的一种算法研究

本文应用在障碍时变工作空间中把固定障碍和时变障碍分解的思想.首先就固定障碍问题,为机器人规划一条无碰撞路径,然后通过规划机器人的速度来达到避开活动障碍的目的.本文接着提出在时间-路径空间中以忽略可动障碍时机器人的运动策略为基准策略,根据障碍约束和机器人速度或加速度约束,用有理二次函数来规划机器人避开可动障碍的运动策略.     

Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.      

Roadmap-based motion planning in dynamic environments

In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.     

基于ART神经网络的机器人避碰撞系统

提出了一种基于ART神经网络的避碰撞系统(ARTCAS)。该系统采用划分区间法来描述障碍物状态,把一个全方位避碰撞问题分解为若干个区间内的避碰撞问题。结合ART神经网络学习新知识而不破坏已有知识的特点,实现了一个具有在线学习和躲避以任意速度任意角度运动的障碍物能力的避碰撞系统。     

Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles (II)

Dynamic path generation problem of robot in environment with other unmoving and moving objects is considered. Generally, the problem is known in literature as find path or robot motion planning. In this paper we apply the behavioral cloning approach to design the robot controller. In behavioral cloning, the system learns from control traces of a human operator. The task for the given problem is to find a controller not only in the form of the explicit mathematical expression. So RBF neural network is used also. The goal is to apply controller for the mobile robot motion planning in situation with infinite number of obstacles. The advantage of this approach lies in the fact that a complete path can be defined off-line, without using sophisticated symbolical models of obstacles.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

Developing a mobile robot for transport applications in the hospital domain

We have been developing MKR (Muratec Keio Robot), an autonomous omni-directional mobile transfer robot system for hospital applications. This robot has a wagon truck to transfer luggage, important specimens, and other materials. This study proposes an obstacle collision avoidance technique for the wagon truck pulling robot which uses an omni-directional wheel system as a safe movement technology. Moreover, this paper proposes a method to reach the goal along a global path computed by path planning without colliding with static and dynamic obstacles. The method is based on virtual potential fields. Several modules with different prediction times are processed in parallel to change the robot response according to its relative velocity and position with respect to the obstacles. The virtual force calculated from each potential field is used to generate the velocity command. Some experiments were carried out to verify the performance of the proposed method. From the experimental results in a hospital it was confirmed that the robot can move along its global path, and reach the goal without colliding with static and moving obstacles.     

Two-dimensional motion-planning for nonholonomic robots using the bump-surfaces concept

In this paper, a new method is introduced for finding a near-optimal path of a nonholonomic robot moving in a 2D environment cluttered with static obstacles. The method is based on the Bump-Surfaces concept and is able to deal with robots represented by a translating and rotating rigid body. The proposed approach is applied to car-like robots.     

On responsiveness, safety, and completeness in real-time motion planning

Replanning is a powerful mechanism for controlling robot motion under hard constraints and unpredictable disturbances, but it involves an inherent tradeoff between the planner’s power (e.g., a planning horizon or time cutoff) and its responsiveness to disturbances. This paper presents an adaptive time-stepping architecture for real-time planning with several advantageous properties. By dynamically adapting to the amount of time needed for a sample-based motion planner to make progress toward the goal, the technique is robust to the typically high variance exhibited by replanning queries. The technique is proven to be safe and asymptotically complete in a deterministic environment and a static objective. For unpredictably moving obstacles, the technique can be applied to keep the robot safe more reliably than reactive obstacle avoidance or fixed time-step replanning. It can also be applied in a contingency planning algorithm that achieves simultaneous safety-seeking and goal-seeking motion. These techniques generate responsive and safe motion in both simulated and real robots across a range of difficulties, including applications to bounded-acceleration pursuit-evasion, indoor navigation among moving obstacles, and aggressive collision-free teleoperation of an industrial robot arm.