Path planning of a multiple mobile robot system

The article presents multiple pattern formation control of the multi-robot system using A* searching algorithm, and avoids the collision points moving on the motion platform. We use speech recognition algorithm to decide the various pattern formations, and program mobile robots to present the movement scenario on the grid-based motion platform. We have been developed some pattern formations to be applied in game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation and so on. The mobile robot contains a controller module, three IR sensor modules, a voice module, a wireless RF module, a compass module, and two DC servomotors. The controller of the mobile robot can acquire the detection signals from reflect IR sensor modules and the compass module, and decide the cross points of the aisle. The mobile robot receives the command from the supervised computer, and transmits the status of environment to the supervised computer via wireless RF interface. We develop the user interface of the multi-robot system to program motion paths for various pattern formation exchanges using the minimum displacement. Users can use speech to control the multiple mobile robots to execut pattern formation exchange. In the experimental results, users can speak the pattern formation. The speech recognition system receives the signal to decide the pattern formation. The multiple mobile robots can receive the pattern formation command from the supervised computer, and arrange the assigned pattern formation on the motion platform, and avoid other mobile robots.

Multiple mobile robot system under decentralized control has become more popular because of advantage of fault tolerance and extensibility. This paper focuses on a planning method for an interactive transportation task by multiple mobile robot system. In this task, task assignment is important to solve interaction among mobile robots. In this paper, we propose the self-generating task assignment algorithm in order to realize efficient sorting task. First, we considered parallelism in transportation task. Second, we defined variable of “Progress” in order to estimate value of tasks which can execute independently and in parallel. Using Progress, robots can select executable parallel task and generate task assignment based on efficiency. We developed multi-robot simulator and multi robot system in order to verify the effectiveness.

The paper is concerned with the region tracking control problem of multiple nonholonomic mobile robot systems with collision avoidance. The peculiar characteristic of the proposed scheme is that a class of potential energy functions is chosen to form the desired target region constraining the robots for aggregation, and some local potential energy functions are selected to maintain collision avoidance among robots. Such that each mobile robot only needs to interact with their neighbors to realize the region tracking control task. Moreover, this paper provides a new analytical framework of region tracking control for a network of wheeled mobile robots, in which the integration of the system decomposition and sliding mode control method enables to convert the nonholonomic constraint problem of the controlled mobile robot system into a holonomic constraint problem. This provides a new approach to solving the mobile robot control problem. Theoretical analysis and numerical simulation results are given to validate the effectiveness of the proposed control scheme.

In this paper, a unique multiple mobile robots system is proposed to enable engineering students and engineers in the field to efficiently learn subsumption architecture and develop swarm intelligence. The subsumption architecture is known as one of the behaviour-based artificial intelligence. Each of multiple mobile robots within the system has three wheels driven by DC motors and six position sensitive detector (PSD) sensors. Network-based subsumption architecture is considered to realise a schooling behaviour by using only information from the PSD sensors. Further, a server supervisory control is introduced for poor hardware platforms with limitations of software development, i.e., the mobile robots can only behave based on the most simply subdivided reaction behaviours, i.e., reflex actions, generated from agents. Experimental results show interesting behaviour among the multiple mobile robots, such as following, avoidance and schooling.

The article describes a new auto-charging system designed to enhance successful docking rates for mobile robots while at the same time monitoring the status of the charging current between the mobile robot and the docking station. The communication interface of the mobile robot and the docking station uses a wireless RF interface. The docking station is designed with one active degree of freedom and two passive degrees of freedom. Users tune the location of the charging pins on the docking station to make it possible for the mobile robot to execute an auto-recharging process. The auto-recharging process uses multiple sensors and a laser range finder located on the mobile robot. The laser range finder searches for a landmark to guide the mobile robot towards the docking station. In the experiment, the power of the mobile robot is under its threshold value. The mobile robot transmits a charging command to the docking station via a wireless RF interface. The docking station transmits a location command back to the mobile robot via this wireless RF interface. The mobile robot uses its laser range finder to search for the landmark of the assigned docking station and programs a motion trajectory to move forward to the docking station. The docking station supplies a charging current to the mobile robot by means of a charger. The power detection module simultaneously detects the charging current and the voltage values of the charging process in both the docking station and the mobile robot. The system can monitor the status of the charging process between the docking station and the mobile robot at any time. The power of the mobile robot is enough to be detected by the power detection module. When the charging process is complete, the docking station turns off the charging current and triggers the mobile robot to leave the docking station via the wireless RF interface.

The article develops multi-pattern formation exchange using A* searching algorithm, and programs the shortest motion paths for mobile robots. The system contains an image recognition system, a motion platform, some wireless RF modules and five mobile robots. We use Otsu algorithm to recognize the variety 2D bar code to classify variety pattern, and control five mobile robots to execute formation exchange, and present the movement scenario on the motion platform. We have been developed some pattern formations according to game applications, such as hook pattern formation, T pattern formation, L pattern formation, rectangle pattern formation, sward pattern formation and so on, and develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange on the supervised computer. The supervised computer programs pattern formation exchange according to the image recognition results, and controls mobile robots moving on the motion platform via wireless RF interface. In the experimental results, mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots on real-time.

The security system of home and building is an important issue to human daily life. We develop a multiple remote interface security system (MRlSS), that integrated with intelligent security robot (ISR), security supervise computer, GSM module, RF interface and appliances control module. The multiple remote interface security system can detect abnormal and dangerous situation and notify us through Internet, or send the message to cellular phone through GSM module. The MRISS can control the appliance control module using wireless RF interface. The user can remote control appliance using cell phone through GSM module, too. The appliance module can feedback reaction result to the user through cell phone. First, we develop the communication protocol in MRISS, and design the multiple interface security robot, security module and appliance control module. Then we implement the MRISS function using the proposed method applying in security detection and appliance control. We can monitor the condition in home or building through Internet, and receive a message of the detection information to cellular phone using wireless RF interface embedded in the ISR. We can also use cell phone to instruct ISR to control appliances. ISR controls appliances via wireless RF module. We have developed and demonstrated the success of the project presented situation here in.

In this work, an algorithm is presented for complete coverage of a grid cell-based environment by mobile robots. The proposed paradigm consists of two biologically inspired neural network models. These allow mobile robots to navigate through collision-free paths and overcome dead-end situations. In this work, inter grid cell diagonal movement is restricted to enhance safety and prevent collision with obstacles. It also ensures inter robot collision-free navigation. The simulation results have shown the effectiveness of the proposed method for a single mobile robot and a multiple mobile robot system. A comparative study is also presented which showcases improvement of the proposed work over the existing literature.

This paper contributes with a multi-level, hierarchical control system for a fleet of mobile robots sharing a common 2D motion space. The system consists of three levels, with the top level being a supervisor based on a discrete representation of the Multiple Mobile Robot System (MMRS), in which robot motion processes are seen as sequences of stages. The supervisor controls centrally the changes of their stages by robots, ensuring their collision-, and deadlock-free concurrent movement. The intermediate control level supervises locally the execution of robot motion on individual stages in a manner consistent with the decisions of the top level. The lowest level, robot control, is responsible for motion execution as determined by the local supervisor. We capitalize on some earlier results concerning the supervisory control of MMRS and propose a common framework for three supervisory control models. Then we propose relevant solutions for the local supervisors, in particular, a DES-based robot-motion-mode control and application of the Artificial Potential Field model for ensuring collision-free motion of two robots sharing a space sector. Next we assume simple robot control and subject the system to simulation experiments aimed at comparing the impact of the different solutions on the performance of MMRS.

The article develops a vision based auto-recharging system for mobile robots, and programs a new docking processing to enhance successful rate. The system contains a docking station and a mobile robot. The docking station contains a docking structure, a control device, a charger and a detection device and a wireless RF interface. The mobile robot contains a power detection module (voltage and current), an auto-switch, a wireless RF interface, a control system and a camera. The docking structure is designed with one active degree of freedom and two passive degrees of freedom. The active degree of freedom can move forward to contact the recharging connect points that are arranged in the mobile robot. The two passive degrees of freedom can rotation in the Z-axis and use compression spring moving on various docking condition. In image processing, the mobile robot uses a webcam to capture the real-time image; and transmits the image signal to the computer via USB interface, and uses Otsu algorithm to recognize the position of the docking station. In the experiment results, the system had been successfully guided the mobile robot moving to the docking station using the proposed method.

This chapter deals with the design and implementation of bio-inspired control architectures for intelligent multiple mobile robot systems. Focusing on building control systems, this chapter presents a non-centralized, behavior-based methodology for autonomous cooperative control, inspired by the adaptive and self-organizing capabilities of biological systems, which can generate robust and complex behaviors through limited local interactions. With autonomous behavior modules for discrete event distributed control, a modular, Petri net-based behavioral control software has been implemented in accordance with a hierarchical distributed hardware structure. The behavior modules with respective pre-conditions and post-conditions can be dynamically connected in response to status events from action control modules at the lower level to achieve the specified overall task. The approach involving planning, control, and reactivity can integrate high-level command input with the behavior modules through the distributed autonomous control architecture.

Distributed motion control is critical in multiple mobile robot systems (MMRSs). Current research usually focuses on either discrete approaches, which aim to deal with high-level collisions and deadlocks without considering the low-level motion commands, or continuous approaches, which can optimize low-level continuous commands to mobile robots but cannot deal with deadlocks efficiently. In this article, by combining discrete and continuous methods, we design a hybrid motion control method for MMRSs where each robot should move along a predefined path. First, each robot’s motion is modeled as a discrete transition system, based on which a real-time supervisory control policy is illustrated to avoid collisions and deadlocks. Second, according to the discrete decisions, the continuous speed at each discrete state is computed using model predictive control and sequential convex programming. The proposed hybrid approach brings two advantages. First, the discrete control component guarantees collision and deadlock avoidance and reduces the scale of the optimization problems. Second, continuous control optimizes the continuous speed in real time and fulfills other performance requirements like time and energy costs. To move in a fully distributed way, each robot needs to predict the motion of its neighbors by retrieving their immediately available information through communications. The simulation and real-world experimental results show the effectiveness of our approach.

The paper develops a fire detection system using mobile robots, and calculates the risk values of the escaping paths using Bayesian estimated method. Mobile robots contain two types moving in the platform. One is fire detection robot (FDR) to search fire sources. The other represents the people walking in the platform autonomously. The controller of the mobile robot detects fire source using flame sensor, and receives the motion command from the supervised compute via wireless RF interface. The mobile robot transmits ID code, position and orientation information, positions of fire sources to the supervised computer via wireless RF interface, too. We program the motion path of fire detection robots to search fire sources, and uses Gauss distribution function to describe the risk values of each fire source. The supervised computer uses Bayesian estimated algorithm to calculate the relation risk value of each cross point for multiple fire sources. In the fire condition, each FDR calculates shortest displacement from the people. The assigned FDR carries the people leaving the dangerous area. Then the user interface programs the escaping paths using A* searching algorithm for mobile robots. The mobile robot guides the people (mobile robot) leaving the fire area according the programmed safety escaping path.

An essential task for a multiple autonomous mobile robot system is to search for and to gather objects. To accomplish such a task effectively, it is essential that all the agents share common information on locations of objects, comrade agents and the place to deposit the objects. The authors have proposed a method called "COMPASS" to realize this only using two functions of each agent-to estimate the location of the objects by dead reckoning and to exchange the information with each other by one to one bidirectional proximity communication. In the paper, a new concept "figure of confidence (FOC)" is introduced to the COMPASS for higher performance and additional capability. The FOC is a self-estimated measure for accuracy of information in each agent. Using the FOC, agents can accelerate information sharing and handle multiple objects without significantly increasing complexity of the algorithm. The validity of FOC is confirmed through computer simulations.

This manuscript looks into details into the hardware implementations of an active stereo vision path planning navigation for a mobile robotics system. This mobile robotics system has been designated as the (KSU-IMR), as it a test-bed for research within the area of mobile robotics system navigation through the use of an active stereo vision for planning a path within an unstructured environment at King Saud University in KSA. The system is making use of fast image processing tools, as in addition to fast algorithms for processing the dynamic images. The approach followed here is to employ the Epipolar Geometry to compute the 3-D details of the environment where the mobile is moving. The algorithm mixes stereo vision, occupancy grid mapping, and potential field path planning procedures to form a robu