Navigation Of Multiple Robots Research Articles

Wireless Communication-aware Path Planning and Multiple Robot Navigation Strategies for Assisted Inspections

Among the many challenges robots encounter in the mining industry, exploring confined environments receives significant attention. This work tackles problems associated with robot communication in hazardous and confined environments, where its cluttered and extensive nature frequently precludes traditional cable-based and wireless solutions. Our methods resort to off-the-shelf long-range radio frequencies to profile the signal propagation behaviour over the geometrical map to assist navigation algorithms that seek to preserve the connection. We consider mathematical models to predict signal power behaviour and serve as input to path planning. We also propose a semi-autonomous leader-follower scheme, with signal repeater units forming a mobile wireless network to enable inspection in hard-to-reach locations. Finally, we present a multi-robot connection-aware system, combining path planning based on radio signal power with multiple robot navigation. Results show the applicability of the proposed solutions, generating single and multi-robot paths for optimal signal reception based on power estimation, thus enabling operations in remote and isolated areas with no line-of-sight between the command base and the robotic inspection device. Experiments conducted in long corridors and in a representative mining environment using the EspeleoRobô and Pioneer platforms demonstrate significant improvements over the traditional communication methods for robotic operation regarding communication quality and inspection range limits.

Sensor fusion based multiple robot navigation in an indoor environment

Sensor fusion based multiple robot navigation in an indoor environment

Guiding Vector Fields for the Distributed Motion Coordination of Mobile Robots

In this article, we propose coordinating guiding vector fields to achieve two tasks simultaneously with a team of robots: first, the guidance and navigation of multiple robots to possibly different paths or surfaces typically embedded in 2-D or 3-D, and second, their motion coordination while tracking their prescribed paths or surfaces. The motion coordination is defined by desired parametric displacements between robots on the path or surface. Such a desired displacement is achieved by controlling the virtual coordinates, which correspond to the path or surface's parameters, between guiding vector fields. Rigorous mathematical guarantees underpinned by dynamical systems theory and Lyapunov theory are provided for the effective distributed motion coordination and navigation of robots on paths or surfaces from all initial positions. As an example for practical robotic applications, we derive a control algorithm from the proposed coordinating guiding vector fields for a Dubins-car-like model with actuation saturation. Our proposed algorithm is distributed and scalable to an arbitrary number of robots. Furthermore, extensive illustrative simulations and fixed-wing aircraft outdoor experiments validate the effectiveness and robustness of our algorithm.

Multi-target trajectory planning and control technique for autonomous navigation of multiple robots

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.

Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.

Distributed supervisory control for multiple robot autonomous navigation performing single-robot tasks

This paper proposes two new approaches based on the Supervisory Control Theory (SCT) of Discrete Event Systems (DES) for autonomous navigation of multiple robots with single-robot tasks being assigned by a centralized scheduler. The two planning strategies differ regarding their implementation, which can be centralized or distributed. Nevertheless, both approaches share the central objective of considering the scheduler and robots as DES, allowing the use of SCT to model and control the behavior of the whole multi-robotic system. Particularly, SCT is used to gather deliberative and reactive motion planning algorithms through a structured procedure, aiming to safely navigate in cluttered, dynamic, partially-known environments. The open-loop behavior of the autonomous navigation system is modeled for both approaches. Moreover, standard supervisors are obtained for the centralized planning approach. In addition, the supervisor localization method is considered to allow a distributed planning approach. Furthermore, as a case study, real experiments considering mobile robots are carried out to corroborate the proposed framework.

Multi-objective trajectory planning of humanoid robot using hybrid controller for multi-target problem in complex terrain

Humanoid robotics is an emerging area of interest in the current engineering research scenario, owing to its ability to impersonate human deportment and emulate various jobs. The given article emphasizes the development and implementation of a hybrid navigational controller to optimize the path length, energy demand, and time spent for accomplishing assigned tasks. The proposed navigational controller is developed by hybridizing the metaheuristic Improved Spider Monkey Optimization (ISMO) approach and the Regression Analysis (RA) approach. Various input parameters like obstacle and target locations are fed to the RA approach that implements a proper navigational direction selection. And it forwards to the SMO approach that is improved using piecewise B-Spline path smoother, which exercises further refinement of the output turning angle and smoothness of path around obstacles. Simulations and real-time experiments are undertaken using different controllers involving single robot systems, which shows the proposed controller’s superiority. An average improvement of 13.72% and 13.94% in path length against RA in simulation and experiment, respectively, and an average improvement of 7.59% and 7.5% in path length against ISMO in simulation and experiment, respectively, is obtained. It is further evaluated for navigation by implementing in a single robot having a multi-target problem. Multiple robot navigation has to deal with the self-collision situations that are solved by prioritizing the specified robot using the dining philosopher controller. It is implemented in the proposed controller for navigation of multiple robots to solve the conflict. Both scenarios are tested in the simulation environment and ratified in the experimental environment. Average deviation under 5% for path length and time spent for single robot navigation and multiple robot navigation is obtained, which shows a good agreement with each other. Energy efficiency test has been performed in contrast to default controller of NAO for various joints, and an average improvement of 8.16%, 5.9% and 20.57%, has been recorded in torque for ankle, knee and hip, respectively. Comparison is carried with an established navigational controller in a similar environmental setup shows an improvement of 8.6% and 10.365%, respectively, in path length and time spent. The results obtained from these setups prove the proposed hybrid controller to be robust, efficient and superior while performing path planning.

Multi-robot path planning using an improved self-adaptive particle swarm optimization

Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

Autonomous Navigation of Multiple Robots with Sensing and Communication Constraints Based on Mixed Reality

This paper presents a robotic navigation system that uses mixed reality concepts to develop sensing and communication virtual devices, based on the visual localization of the robot in the environment. The main objective of the navigation system is to provide conditions for the use of very simple robots with severe limitations on the mentioned peripheral devices for simulation, analysis and test of multi-robot applications. In an experiment with real robots, each one receives its virtual navigation skills in an independent way from the tool that emulates the function of such peripherals. Thus, the behavior of a group of robots, independently commanded, is implemented in the virtual environment and accomplished in the real world. An experiment composed by real multiple Sphero robots executing an exploratory task within an unknown dynamic environment is carried out to validate the proposed navigation system. The use of mixed reality concepts allows an easy implementation of cooperation mechanisms based on indirect communication skill and fuzzy controllers for the robots’ movement. The results confirm the feasibility of the proposed autonomous navigation system.

Deployment and navigation of multiple robots using a self-clustering method and type-2 fuzzy controller in dynamic environments

Deployment and navigation of multiple robots using a self-clustering method and type-2 fuzzy controller in dynamic environments

Multiple Mobile Robot Dispatch Strategy for Cooperative Applications in Confined Space

In this paper, we propose a path planning and dispatch strategy of multiple mobile robots for cooperative applications in confined spaces. The proposed two level path planning method incorporates the segment reservation mechanism to maximize the utilization of robots. The two level path planning reduces the computational complexity while providing the flexibility for real-time adaptation. Any potential deadlock is completely eliminated and the starvation due to unpredictable service request patterns is avoided. Since the proposed methodology is used for confined areas, any possible path blockage due to idling robots is eliminated with idling robot relocation mechanism. The dynamic path planning is achieved by monitoring and controlling the speed of the robots. The dynamic replanning is triggered with periodic monitoring of the service constraints. The method can be effectively used for low power multiple robot navigation applications and the performance of the proposed system is evaluated with largescale simulations.

Avoiding blind leading the blind

Virtual pheromone trailing has successfully been demonstrated for navigation of multiple robots to achieve a collective goal. Many previous works use a pheromone deposition scheme that assumes perfect localization of the robot, in which, robots precisely know their location in the map. Therefore, pheromones are always assumed to be deposited at the desired place. However, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot and the dynamics of the environment in which the robot operates. In real-world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a “blind leading the blind” scenario that reduces the efficiency of the multi-robot system. We propose a pheromone deposition algorithm, which models the uncertainty of the robot’s pose. We demonstrate, through experiments in both simulated and real environments, that modeling the uncertainty in pheromone deposition is crucial, and that the proposed algorithm can model the uncertainty well.

2A2-G07 群れにおけるリーダー観測数の変化が速度合意に与える影響

This paper presents a solution to the problem of multiple robot navigation by an approach called Swarm Leading. We develop and analyze a method to form an effective network by introducing three kinds of robot about communication based on the approach. The simulation results verify the proposed method.

Real‐time deadlock‐free navigation for multiple mobile robots

AbstractThis paper proposes a practicable navigation method for multiple mobile robots in a realistic environment. In the past, many navigation methods have been developed. However, they were often limited to a single robot and sometimes assumed robots with special mobility such as holonomic ones. From the viewpoint of practical applications it is indispensable that the number of robots is arbitrary and a general shape and mobility for them is allowed.In this study, deadlock‐free navigation for nonholonomic mobile robots in a practical environment is given. In the proposed scheme, states of the environment and robots are fed back in real time, and global path planning is cyclically executed. This enables an adaptation to a changing environment and reliable, deadlock‐free navigation for multiple robots. The real‐time online path planning is performed by an efficient A* search in Configuration Spaces representing the robots and environment.The proposed method is tested in several simulations which represent typical complicated navigation situations. As a result, the effectiveness of the method is verified. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 27– 36, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20714

Leader-Following Formation Navigation for Multiple Robots with Collision Avoidance

This paper deals with a collision avoidance problem in leader-following formation navigation for multiple mobile robots. Because followers should move along a leader's trajectory, we first try to avoid collisions by adjusting their velocities on the trajectory. This strategy causes delays of the followers from the leader, which is often problematic because we cannot predict how late the followers will be from the leader. Moreover, there are situations that the robots cannot avoid collisions only with this strategy. If the leader goes straight and turns back toward the followers suddenly, the followers have to move back along the trajectory where the leader just moved. In this case, the followers would be better off moving around to the back of the leader and recover their delays even if they go away from the specified trajectory. From these viewpoints, this paper proposes a collision avoidance method for leader-following formation navigation taking into account both the tracking errors and the delays of the followers from the leader. This method adjusts the velocity of each follower, as well as modifies the shape of the trajectory if a delay becomes too large. The effectiveness of the proposed method is demonstrated by a simulation with three mobile robots.

複数台移動ロボットのためのデッドロックフリーナビゲーション法

This paper proposes a practicable navigation method for multiple mobile robots in a realistic environment. In the past, many navigation methods have been developed. However, they were often limited to a single robot and sometimes assumed robots with special mobility such as holonomic ones. From the viewpoint of practical applications it is indispensable that the number of robots is arbitrary and a general shape and mobility for them is allowed. In this study, deadlock-free navigation for nonholonomic mobile robots in a practical environment is given. In the proposed scheme, states of the environment and robots are fed back in real time, and global path planning is cyclically executed. This enables an adaptation to a changing environment and reliable, deadlock-free navigation for multiple robots. The real-time online path planning is performed by an efficient A* search in Configuration Spaces representing the robots and environment. The proposed method is tested in several simulations which represent typical complicated navigation situations. As a result, the effectiveness of the method is verified. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 27– 36, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20714

A feedback stabilization and collision avoidance scheme for multiple independent non-point agents

A navigation functions’ based methodology, established in our previous work for centralized multiple robot navigation, is extended to address the problem of decentralized navigation. In contrast to the centralized case, each agent plans its actions without knowing the destinations of the other agents. Asymptotic stability is guaranteed by the existence of a global Lyapunov function for the whole system, which is actually the sum of the separate navigation functions. The collision avoidance and global convergence properties are verified through simulations.

Real-time path planning of autonomous robots in a two-dimensional unknown dynamic navigation environment

This paper deals with the real-time path planning of an autonomous mobile robot in two-dimensional, unknown, dynamic multiple robot navigation space. In particular, a collision-free navigation path planning strategy is presented in real time by using a heuristichuman like approach. The heuristic scheme used here is based on thetrial and error methodology with the attempt to minimize the cost of the navigation efforts, when time plays a significant role. Past built-up navigation experience and current extracted information from the surrounding environment are used for the detection of other moving objects (robots) in the same navigation environment. Moreover, the determination of asecure navigation path is supported by a set of generic traffic priority rules followed by the autonomous robots moving in the same environment. Simulated results for two moving objects in the same navigation space are also presented.