A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

Cellular ants: A method to create collision free trajectories for a cooperative robot team

Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method.     

通讯受限条件下的机器人编队算法

针对通讯受限条件下大规模移动机器人编队任务, 本文提出了基于行为的分布式多机器人线形编队控制 和避障算法. 机器人个体无需获得群体中所有机器人的信息, 而是根据传感器获取的环境信息和局部范围内的机器 人信息对其自身的调整方向进行预测, 并最终很好地完成了设定的编队及避障任务. 由于本文方法需求的通讯量不 大, 并且采用分布式控制, 因此该方法适用于大规模的机器人集群编队任务. 文中还给出了本系统的稳定性分析, 证 明了系统的稳定性. 实验结果表明该算法使得机器人能够仅通过局部信息形成线形编队, 在遇到障碍物后能够灵活 避开障碍物, 并且在避开障碍物进入安全区域后重新恢复线形编队.     

Reinforcement learning-based dynamic obstacle avoidance and integration of path planning

Deep reinforcement learning has the advantage of being able to encode fairly complex behaviors by collecting and learning empirical information. In the current study, we have proposed a framework for reinforcement learning in decentralized collision avoidance where each agent independently makes its decision without communication with others. In an environment exposed to various kinds of dynamic obstacles with irregular movements, mobile robot agents could learn how to avoid obstacles and reach a target point efficiently. Moreover, a path planner was integrated with the reinforcement learning-based obstacle avoidance to solve the problem of not finding a path in a specific situation, thereby imposing path efficiency. The robots were trained about the policy of obstacle avoidance in environments where dynamic characteristics were considered with soft actor critic algorithm. The trained policy was implemented in the robot operating system (ROS), tested in virtual and real environments for the differential drive wheel robot to prove the effectiveness of the proposed method. Videos are available at https://youtu.be/xxzoh1XbAl0.

     

A hierarchical long short term safety framework for efficient robot manipulation under uncertainty

Safe and efficient robot manipulation in uncertain clustered environments has been recognized to be a key element of future intelligent industrial robots. Unlike traditional robots that work in structured and deterministic environments, intelligent industrial robots need to operate in dynamically changing and stochastic environments with limited computation resources. This paper proposed a hierarchical long short term safety system (HLSTS), where the upper layer contains a long term planner for global reference trajectory generation and the lower layer contains a short term planner for real-time emergent safety maneuvers. Additionally, a hierarchical coordinator is proposed to enable smooth coordination of the two layers by compensating the communication delay through trajectory modification. The theoretical results verify that the long term planner can always find a feasible trajectory (feasibility guarantee); and the short term planner can guarantee safety in the probabilistic sense. The proposed architecture is validated in industrial settings in both simulations and real robot experiments, where the robot is interacting with randomly moving obstacles while performing a goal reaching task. Experimental results demonstrate that the proposed HLSTS framework not only guarantees safety but also improves task efficiency.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

Grafting: A Path Replanning Technique for Rapidly-Exploring Random Trees in Dynamic Environments

The rapidly-exploring random trees (RRT) is a sampling-based path planner which utilizes simultaneously kinematics and dynamics of a robot. However, since the RRT has produced a robot path without taking the existence of dynamic obstacles into consideration, RRT-based navigation has the risk of a collision with dynamic obstacles. We proposed a path replanning technique for the RRT applied to robot navigation in dynamic environments, which is named grafting. The proposed technique replans a safe and efficient path in real time instead of the original path which may cause a collision with dynamic obstacles. Moreover, the replanned path can be easily merged into the original RRT path because the grafting technique preserves the property of the RRT. The grafting technique was tested by simulations in various dynamic environments, which revealed that the grafting technique was capable of replanning a safe and efficient path for RRT-based navigation in real time.     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。     

智能机器人系统中局部环境特征的提取

本文基于栅格地图和滚动视窗的控制方法，提出了一种提取机器人局部障碍物群环境特征的 数据融合新方法．该方法在多个级别对原始数据进行不同程度的抽象和压缩，减少机器人内 部模块之间或机器人之间、机器人与控制中心进行通讯的数据量，提高系统的动态性能．同 时，该方法对复杂环境具有良好的自适应性和实时性．本文分别列举了仿真实验和物理实验 结果，表明了机器人采用障碍物群的环境特征提取方法可以成功地完成躲避障碍物和路径规 划的任务．     

Enhanced path smoothing based on conjugate gradient descent for firefighting robots in petrochemical complexes*

The firefighting robot system (FFRS) comprises several autonomous robots that can be deployed to fire disasters in petrochemical complexes. For autonomous navigation, the path planner should consider the robot constraints and characteristics. Specifically, three requirements should be satisfied for a path to be suitable for the FFRS. First, the path must satisfy the maximum curvature constraint. Second, it must be smooth for robots to easily execute the trajectory. Third, it must allow reaching the target location in a specific heading. We propose a path planner that provides smooth paths, satisfy the maximum curvature constraint, and allows a suitable robot heading. The path smoother is based on the conjugate gradient descent, and three approaches are proposed for this path planner to meet all the FFRS requirements. The effectiveness of these approaches is qualitatively and quantitatively evaluated by examining the generated paths. Finally, the path planner is applied to an actual robot to verify the suitability of the generated paths for the FFRS, and planning is applied to another type of robot to demonstrate the wide applicability of the proposed planner.     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

Mapping and exploration in a hierarchical heterogeneous multi-robot system using limited capability robots

This paper focusses on the development of a customised mapping and exploration task for a heterogeneous ensemble of mobile robots. Many robots in the team may have limited processing and sensing abilities. This means that each robot may not be able to execute all components of the mapping and exploration task. A hierarchical system is proposed that consists of computationally powerful robots (managers) at the upper level and limited capability robots (workers) at the lower levels. This enables resources (such as processing) to be shared and tasks to be abstracted. The global environment containing scattered obstacles is divided into local environments by the managers for the workers to explore. Worker robots can be assigned planner and/or explorer tasks and are only made aware of information relevant to their assigned tasks.     

Integrating explicit path planning with reactive control of mobile robots using Trulla

This article describes and investigates a method of interleaving explicit path planning with reactive control. The Trulla all-paths planner computes an a priori set of optimal paths. Minor reactions to obstacles and terrain changes serve to switch the robot from the precomputed path to a new precomputed path, eliminating subgoal obsession. Major deviations suggest that the a priori map is significantly wrong; explicit replanning should be triggered to ensure continued progress of the robot. The dot product is used as the intrinsic measure of a major deviation. This methodology is particularly well-suited for computationally bound robots such as planetary rovers and robots operating in indoor environments with a large number of minor unmodeled obstacles.

The article describes the Trulla and dot product algorithms, and reports on experimental data collected from a mobile robot under representative scenarios. The method is compared to continuous and fixed frequency replanning under differing environments and robot velocities. The results show that the deferred replanning with the Trulla/dot product methodology produced actual paths similar to more frequent replanning in distance and time but with up to 100 times less computation. The reduced computation led to a 8.75% increase in distance traveled and 24% increase in travel time. In the presence of faulty sensor data, Trulla outperformed the other methods which radically changed the path back and forth due to spurious sensor readings.     

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.     

Reinforcement learning-based group navigation approach for multiple autonomous robotic systems

In several complex applications, the use of multiple autonomous robotic systems (ARS) becomes necessary to achieve different tasks, such as foraging and transport of heavy and large objects, with less cost and more efficiency. They have to achieve a high level of flexibility, adaptability and efficiency in real environments. In this paper, a reinforcement learning (RL)-based group navigation approach for multiple ARS is suggested. Indeed, the robots must have the ability to form geometric figures and navigate without collisions while maintaining the formation. Thus, each robot must learn how to take its place in the formation, and avoid obstacles and other ARS from its interaction with the environment. This approach must provide ARS with the capability to acquire the group navigation approach among several ARS from elementary behaviors by learning with trialand-error search. Then, simulation results display the ability of the suggested approach to provide ARS with capability to navigate in a group formation in dynamic environments. With its cooperative behavior, this approach makes ARS able to work together to successfully fulfill the desired task.     

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.     

Queues and Artificial Potential Trenches for Multirobot Formations

In this paper, we present a novel approach for representing formation structures in terms of queues and formation vertices, rather than with nodes, as well as the introduction of the new concept of artificial potential trenches, for effectively controlling the formation of a group of robots. The scheme improves the scalability and flexibility of robot formations when the team size changes, and at the same time, allows formations to adapt to obstacles. Furthermore, for multirobot teams to operate successfully in real and unstructured environments, the instant goal method is used to effectively solve the local minima problem.