基于轨迹引导的移动机器人导航策略优化算法

针对在杂乱、障碍物密集的复杂环境下移动机器人使用深度强化学习进行自主导航所面临的探索困难,进而导致学习效率低下的问题,提出了一种基于轨迹引导的导航策略优化（TGNPO）算法。首先,使用模仿学习的方法为移动机器人训练一个能够同时提供专家示范行为与导航轨迹预测功能的专家策略,旨在全面指导深度强化学习训练;其次,将专家策略预测的导航轨迹与当前时刻移动机器人所感知的实时图像进行融合,并结合坐标注意力机制提取对移动机器人未来导航起引导作用的特征区域,提高导航模型的学习性能;最后,使用专家策略预测的导航轨迹对移动机器人的策略轨迹进行约束,降低导航过程中的无效探索和错误决策。通过在仿真和物理平台上部署所提算法,实验结果表明,相较于现有的先进方法,所提算法在导航的学习效率和轨迹平滑方面取得了显著的优势。这充分证明了该算法能够高效、安全地执行机器人导航任务。     

Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

多移动机器人轨迹跟踪控制系统设计与实现

针对轮式移动机器人的轨迹跟踪控制问题,在分析了机器人运动学模型的基础上,构建多机器人的领航-追随模型;采用跟踪微分器在输入输出两端安排过渡过程,设计了一种基于多变量解耦的非线性PID轨迹跟踪控制器;搭建以Arduino Mega 1280控制板为核心的移动机器人实验平台,采用速度PID控制器以满足机器人驱动电机的实时调速要求,基于ROS提出一种结构化和模块化的多机器人控制系统;在此基础上进行实验,并将实验结果与传统PID方法控制的实验结果进行对比;实验结果验证了文章所提算法的有效性,控制器易于实现且具有一定的鲁棒性。     

Deliberative On-Line Local Path Planning for Autonomous Mobile Robots

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.     

Navigation strategy of multiple mobile robot systems based on the null-space projection method

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.     

基于激光雷达与双目视觉的移动机器人SLAM研究

为解决移动机器人在环境未知条件下,利用单一传感器自主导航时不能及时定位、构建地图不精确的问题,提出采用一种改进RBPF算法,在计算提议分布时将移动机器人的观测数据(视觉信息与激光雷达信息)和里程计信息融合;针对一般视觉图像特征点提取算法较慢的问题,采用基于ORB算法对视觉图像进行处理以加快视觉图像处理速度的方法;最后通过在安装有开源机器人操作系统(ROS)的履带式移动机器人进行实验,验证了采用该方法可构建可靠性更高、更精确的2D栅格图,提高了移动机器人SLAM的鲁棒性.     

单目视觉移动机器人导航算法研究现状及趋势

拥有自主导航能力的移动机器人在救灾、家政等人类生活中使用得愈加广泛。单目视觉导航算法作为机器人视觉导航中的一种,具有成本低、距离不受限的优势,但仍存在尺度不确定性和初始化问题。该综述根据对移动机器人的运动性质研究,主要从障碍检测、空间定位、路径规划三个方面对单目视觉导航技术进行了模块化分析,并以单目视觉导航算法的关键技术迭代与发展为脉络,对各模块的典型算法进行分析,从速度、准确性、鲁棒性等方面对不同算法进行综合性评比,并对算法存在的主要问题与难点进行剖析,结合人类对移动机器人能力的需求和移动机器人的技术状态对单目视觉导航技术的未来发展趋势进行预测。     

Path planning of wheeled mobile robot with simultaneous free space locating capability

This paper presents a hybrid path planning algorithm for the design of autonomous vehicles such as mobile robots. The hybrid planner is based on Potential Field method and Voronoi Diagram approach and is represented with the ability of concurrent navigation and map building. The system controller (Look-ahead Control) with the Potential Field method guarantees the robot generate a smooth and safe path to an expected position. The Voronoi Diagram approach is adopted for the purpose of helping the mobile robot to avoid being trapped by concave environment while exploring a route to a target. This approach allows the mobile robot to accomplish an autonomous navigation task with only an essential exploration between a start and goal position. Based on the existing topological map the mobile robot is able to construct sub-goals between predefined start and goal, and follows a smooth and safe trajectory in a flexible manner when stationary and moving obstacles co-exist.     

Navigation of mobile robot by using D++ algorithm

The navigation of mobile robots is a vital aspect of technology in robotics. We applied the D++ algorithm, which is a novel and improved path-planning algorithm, to the navigation of mobile robots. The D++ algorithm combines Dijkstra??s algorithm with the idea of a sensor-based method, such that Dijkstra??s algorithm is adapted to local search, and the robot can determine its next move in real-time. Although the D++ algorithm frequently runs local search with limited ranges, it can compute optimum paths by expanding the size of the searching range to avoid local minima. In addition, we verified the performance of the D++ algorithm by applying it to a real robot in a number of environments. The use of the D++ algorithm enables robots to navigate efficiently in unknown, large, complex and dynamic environments.     

基于路径跟踪的拖车式移动机器人动态窗口法

针对目前局部路径规划算法只适用于单车体机器人的问题,提出了一种针对拖车式移动机器人的动态窗口法。首先,利用多车体结构的路径跟踪方程实现对拖车式移动机器人的运动控制;然后,利用评价函数同时对牵引车和拖车进行评价并根据权重相加;最后,针对拖车结构特性,添加了运动过程中牵引车与拖车的夹角约束,保证运动轨迹的稳定性。仿真实验表明:拖车式移动机器人的运动控制可满足收敛性,同时所提算法实现了拖车式移动机器人局部路径规划的任务,且在运动过程中夹角变化均未超出限制。该研究对拖车式移动机器人的自主导航有极大的参考价值。     

基于行为优先与模糊控制的移动机器人导航

由于动态未知环境下自主移动机器人的导航具有较大困难,为实现自主机器人在动态未知环境下的无碰撞运行,文中将行为优先级控制与模糊逻辑控制相结合,提出4种基本行为控制策略:目标寻找、避障、跟踪和解锁.针对'U'型和'V'型障碍物运行解锁问题,提出了行走路径记忆方法,并通过构建虚拟墙来避免机器人再次走入此类区域.仿真实验表明,所提出的控制策略可有效地运用于复杂和未知环境下自主移动机器人的导航,且具有较好的鲁棒性和适应性.     

Mobile Robot Navigation for Moving Obstacles with Unpredictable Direction Changes,Including Humans

In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.     

分布式视觉机器人导航中的定位算法研究

生物启发的无线复眼导航技术是新型的机器人导航方案,将分布在环境中的分布式智能代替了传统的集中式智能。蒙特卡洛定位是近来流行的机器人自主定位算法,将这种算法应用在分布式视觉传感器机器人的定位中,并针对多视觉传感器观测值的最优选择,提出了一种分布式的基于熵的观测量选择方法,目的是选择那些对提高定位精度更有效的观测信息,在保证定位精度的前提下,提高了定位的实时性和可靠性。仿真实验结果证明了这种算法的可行性。     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

Probabilistic Road Map sampling strategies for multi-robot motion planning

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.     

分布式视觉机器人导航中的定位算法研究

生物启发的无线复眼导航技术是新型的机器人导航方案,将分布在环境中的分布式智能代替了传统的集中式智能。蒙特卡洛定位是近来流行的机器人自主定位算法,将这种算法应用在分布式视觉传感器机器人的定位中,并针对多视觉传感器观测值的最优选择,提出了一种分布式的基于熵的观测量选择方法,目的是选择那些对提高定位精度更有效的观测信息,在保证定位精度的前提下,提高了定位的卖时性和可靠性。仿真实验结果证明了这种算法的可行性。     

移动机器人视觉导航控制研究

该文研究了移动机器人视觉导航的控制问题。针对导航中的图像畸变以及视野有限易造成导航线丢失等问题,提出了一种简单的单目视觉目标定位算法和一种新的控制策略。在导航时,首先利用定位算法精确地获取地面目标的深度信息,然后控制机器人沿一系列切线方向平滑接近导航线(或目标),并根据实施控制的时间间隔控制速度,以保证机器人视野中导航线(或目标)不丢失。实际的应用证明了该定位算法和策略的有效性。     

基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.