205 results about "Multirobot systems" patented technology

A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex tasks.

The invention provides a method for collaborative mapping and locating of multiple robots for a large-scale environment. The method comprises a single-robot laser SLAM algorithm based on a visual detection closed loop, a multi-robot pose constraint estimation algorithm and a multi-robot map fusion algorithm, wherein according to the single-robot laser SLAM algorithm based on the visual detection closed loop, a visual sensor is adopted for assisting a laser sensor in achieving the SLAM algorithm with the more stable roughness. Simultaneous locating and mapping of the multiple robots are achieved through the laser sensor and the visual sensor. The closed loop is detected by obtaining the visual characteristic of the roughness through a camera, and the problem about closed loop detection caused by the robot motion accelerative error is solved effectively; meanwhile through a multi-robot system, simultaneous locating and mapping in the large regional environment are completed efficiently, and the defect that the efficiency is low by means of a single robot is overcome. By the adoption of the method, the precise robot location and map creation of the environment are achieved in the large-scale environment, and the method is also suitable for small-scale environments.

The invention discloses a multi-robot formation method, belonging to the field of intelligent control. The method comprises the following steps of: controlling the whole formation motion trail by the leader motion trail, firstly, determining a kinematics model of the leader, and determining the direction of the motion of the leader according to a resultant force of a repulsive force and a gravitational force; creating a motion model of following the leader by the follower, following the leader by the follower according to certain distance and angle, and determining the motion trail of the follower according to a motion model created by the artificial potential field; introducing an AdHoc between the leader and the follower, creating information feedback, and ensuring that no loss occurs in the process of following the leader by the follower. With the method provided by the invention, a multi-robot system can successfully avoid obstacles in the process of finishing tasks to reach a target point, and also can keep initial order in the whole process, implement real-time order control on multiple robots and be more suitable for some occasions where multiple robots are needed for finishing tasks (such as transporting, rescuing and the like) synchronously.

Based on the mine searching and detecting multiple robots of wireless sensor networks, the invention relates to the searching and detecting field for mines and underground accident site, which overcomes the problem that the existing technology can not transmit the site environment information and personnel information rapidly. The invention consists of the following devices: a multiple-joint robot which is provided with a long and thin structure in a snake shape, adopts the tract type walking mode, and is provided with a boarded bin that can carry a plurality of small investigation robots; a plurality of sensors carried by the robot which can confirm the location of the miners in danger and can detect the kinds, content, and environment temperature of harmful gases; small investigation robots, which realize the multiple-points information collection and long-distance communication through being released along the way; a remote operation and control terminal, which is arranged on the ground, forms a wireless sensor network with the multiple-joint detection robot and the small investigation robots, and builds the communication link between the operator and the robots; the rescuing personnel can operate each part of the robot in a remote end on a real-time basis and can obtain the site environment information and the information of the personnel in danger.

A system is provided that includes at least one manager and one or more robots configured to communicate wirelessly. The manager can include certain functions that generate data, instructions, or both used by one or more robots. The manager can also facilitate communications among several robots, or robots could also be configured to communicate directly.

The invention discloses a scheduling, organization and cooperation system and a scheduling, organization and cooperation method for a multi-robot system. By adoption of distributive mode architecture, the information interaction is reduced, the systematic resources are used fully, the activity and the problem solving level of the overall system are enhanced, and the ability of intelligent coordination and flexible cooperation among a plurality of robots is enhanced. The method and the system intelligently perform scheduling and cooperation, reduce the communication traffic, reduce the systematic consumption, well adapt to a dynamic environment and complicated dynamic tasks, and enhance the problem solving capability by employing a dynamic decision maker scheme, namely every robot may be atask decision maker. According to the invention, a bulletin is used, a method of making a robot actively check the bulletin to acquire task information in an active free (capability) state is adoptedand the selection of the best can be finished during checking, so that the information interaction is reduced, the information processing and comparison process is simplified, the communication traffic is greatly reduced and problems of information blocking and wastes of the systematic resources are solved.

The invention belongs to the field of intelligent movable robots, and discloses a layered topological structure based map splicing method for a multi-robot system in an unknown environment and solves the map splicing problem of the multi-robot system in case the position and posture are unknown. The method comprises the following steps: acquiring an accessible space tree, building a layered topological structure, creating a global topological map, extracting SIFT (Scale Invariant Feature Transform) features and performing feature matching, and performing map splicing based on ICP (Iterative Closest Point) scanning matching. According to the invention, under the condition that the relative positions and gestures of robots are unknown, a layered topological structure merging SIFT features is provided, the global topological map is created in an increment manner, and the map splicing of the multi-robot system under large scale unknown environment is realized according to the SIFT information among the nodes, in combination with a scanning matching method; and the splicing accuracy and real-time performance are effectively improved. Therefore, the method is suitable for the field of intelligent mobile robots related to map creation and map splicing.

The invention discloses a track teaching and planning method of a master-slave mode multi-robot cooperative system. The method comprises the steps of: classifying the cooperative movement of the multi-robot system into a coupling movement and a superposition movement according to the relative movement modes between the tail end poses of the cooperative robots, and deducing the constraint relation between the tail end poses of the robots according to the two relative movement modes; and determining the track teaching steps and teaching information of the multi-robot cooperation according to the constraint relation, and converting the cooperative movement tack planning problem of the multi-robot cooperative system into Cartesian space track planning problem of a plurality of single robots so as to finish the track teaching and planning task of the multi-robot cooperative system. The track teaching and planning method disclosed by the invention can be applied in multiple service processes meeting the movement constrain, for example, partial cooperative movement in complex assembly task, carrying of large-size workpiece and non-fixture welding process, and the like, so that the track teaching and planning method has an extensive application prospect.

The invention, which belongs to the robot system control field, relates to a distributed adaptive-neural-network continuous tracking control method of a multi-robot system. According to the existing coordinated tracking and controlling method of the multi-robot system, problems of parameter uncertainty and external interference existence in the multi-robot system exist. The provided method comprises: under the circumstances that only parts of followers can obtain dynamic navigator state information, a distributed observer design is implemented with limitation of communication tine delay existence, so that all followers can obtain the dynamic navigator state information; and with consideration of the parameter uncertainty and external interference existence in the system, controlling is carried out by using a distributed adaptive tracking control expression designed based on two neural networks, so that the approximate error is close to zero. In addition, the control algorithm of the distributed adaptive tracking control expression is in a continuous control mode, no buffet is caused at the system and the great practical application value is created. Besides, validity of the control algorithm is verified by the simulation experiment.

A system is provided that includes at least one manager and one or more robots configured to communicate wirelessly. The manager can include certain functions that generate data, instructions, or both used by one or more robots. The manager can also facilitate communications among several robots, or robots could also be configured to communicate directly.

The present invention provides a multi-robot task allocation method based on multi-objective optimization. A weighted summation mode is adopted to carry out modeling on a time utility objective and an energy utility objective and the time utility objective and the energy utility objective are optimized so as to implement multi-robot task allocation; energy utility is used for evaluating energy consumption of robots in the task completing process; and time utility is used for evaluating time consumption of the robots in the task completing process. According to the method, an evaluation mechanism of two factors, i.e. the time consumption and the energy consumption, can be effectively shown in the multi-robot task completing process, and thus, computing time is short and the optimal multi-robot task allocation scheme set can be rapidly obtained, so that task allocation time is greatly reduced and task completing efficiency is improved. According to the present invention, the problem of task allocation in a multi-robot system is more comprehensively and more systematically solved, a task allocation result quality quantitative evaluation index based on the mechanism is increased and improvement on scientificity and reasonability of a task allocation result is realized.

A finite time robust cooperative tracking control method for a multi-robot system relates to a control method for a multi-robot system, and solves the problems that a conventional control method for a multi-robot control system is poor in robustness and the overall communication burden of the multi-robot system is too heavy. The method includes the steps: establishing a dynamics model, shown in the description, of each follower robot in the multi-robot system, wherein the dynamics model can be linearized as defined in the description; defining variables qri, zli and z2i; combining a virtual controller [alpha]1i to obtain a formula shown in the description; and designing a distributed control law shown in the description and a linear parameter self-adaptive law shown in the description. In this way, each follower robot can follow, within the finite time, a leader robot that has a dynamic time-varying track, tracking errors are bounded, and then finite time tracking control over the multi-robot system is completed. The method is suitable for the control field of a multi-robot system.

The invention discloses a self-adaptive hunting device using multiple robot pursuers to hunt a single moving target and a method. The method comprises multiple-stage dividing of a hunting process, a pursuing policy based on a bionic neural network, angular relationship surrounding and multiple virtual potential points. According to the step of multiple-stage dividing of the hunting process, multiple-stage modeling is carried out for the hunting process; a hunting task is divided into four stages of searching, pursuing, surrounding and arresting; and policies corresponding to four stages are used for controlling. According to the pursuing policy based on the bionic neural network, a bionic neural network method used in a multiple-robot system is migrated to a hunting environment, and hunting guide is carried out on multiple robot pursuers. According to angular relationship surrounding, an angular relationship is used to adjust the movement direction of the pursuers. According to multiple virtual potential points, multiple virtual potential points are arranged to form an arresting formation, and the final hunting task is completed. According to the invention, the hunting process is divided into different stages; in the pursuing stage, the bionic neural network method is used to solve uncertainty, dynamics and instantaneity of an environment; and efficient obstacle avoidance and hunting are completed.

The present invention discloses a complete coverage path planning method for a multi-robot system. This method combines a biologically inspired neural network model with a backtracking mechanism to reduce the repeated coverage path and shorten the time needed by complete coverage. The method concretely comprises the following steps: every robot uses the biologically inspired neural network model to cover surrounding environment until the robot enters a deadlock state, robots use the backtracking mechanism to select an optimum backtracking node without waiting for the attenuation of a neuron activity value, and a dynamic A star algorithm is used to plan out a path to the backtracking node; and the robots continue to enter a coverage mode after moving to the backtracking node. When one of the robots is deadlocked, the backtracking mechanism of the invention chooses the most suitable backtracking node according to a market mechanism, and the bidding process in the market mechanism considers the path length of the robot during backtracking and the conflict relationship with other robots, so the total time needed by the complete coverage is greatly shortened.

The present invention relates generally to film wrapping or packaging systems, and more particularly to a new and improved robotic film wrapping or packaging system which is not only substantially entirely automatic in its operation except for its initialization or START procedure, but in addition, a single programmable logic controller (PLC) can control the cyclic operations of one or more film wrapping or packaging robots.

The invention belongs to the field of automatic control, in particular relates to a task allocation method of a heterogeneous multi-robot system, which comprises the following steps: S1, initializing robots and subtask parameters; S2, executing subtasks with high information concentration near the robots and close to self-capacity and subtask ability requirement by the robots, wherein the robots are skilled in the subtasks. The invention fully considers the influence of a functional structure of the multi-robot system to the task allocation of the multi-robot system, and realizes the task allocation of the heterogeneous multi-robot system.

The present invention provides a path planning algorithm for the multi-robot team formation in a three-dimensional space based on the market auction method and the tangent circle method and belongs to the field of intelligent algorithm optimization. According to the invention, the motion simulation for four robots to be in a spatial tetrahedron team formation is realized. The task allocation is conducted based on the market auction method, so that each robot of a multi-robot system can acquire a task coordinate azimuth in the low-energy-consumption and low time-consuming condition. Based on the traditional tangent circle method, a tangent circle method in the three-dimensional space is provided, and the motion path of each robot is planned. Finally, the shape of a team formation is formed, so that all robots are the same in motion posture. The result of simulation studies show that, the above method is short in tem formation time and low in energy consumption, which can realize the path planning task for the multi-robot team formation in the three-dimensional space. The effectiveness and the practicability of the algorithm are verified.

A coordination-control system of multi-type bio-robots is prepared for using two sets of video cameras to collect ambient information of ground and underwater, sending collected information separately into image collection card of control computer at ground through cable, generating control commands by said control computer according to position and direction as well as target information of each bio-robot at different ambient, transmitting control commands to radio receiving module of each bio-robot through various radio emitting modules connected with serial interface expansion module for guiding movements of each bio-robot.

The invention relates to an intelligent control method for coordinating a multiple-robot system. The multiple-robot system consumes excessive energy in the traditional method. The intelligent control method comprises the following steps of: according a kinematic model of a smell source releasing smell molecules, establishing an observation mode of a robot to the position of the smell source; within each sampling period, in case of any smell detected, estimating the position of the smell source according to Kalman filtering theory and the observation value of the robot to the position of the small source; based on the estimation value of the robot detecting the thickest smell source in the group to the position of the smell source, updating the estimation value of each robot to the position of the smell source, and using the estimation of the robot to the position of the smell source as the basis of estimation of the next position for the robot. The invention effectively improves the precision of positioning the smell source, guarantees less consumption of energy of the robot system, and meets the requirement for quick positioning in actual demands.

The invention relates to a trajectory prediction method for multiple mobile robots, which comprises the steps of 1) building a multi-mobile-robot motion model in a complex plane; 2) building a topological graph of a multi-robot system; 3) solving a complex Laplacian matrix; 4) solving a stable matrix; 5) calculating a distributed discrete control signal; and 6) recording displacement information and calculating the final position. According to the invention, each robot can be enabled to predict a trajectory path thereof in the future within the shortest time, the method is completely distributed, and the utilized information is only the displacement information recorded by the robots by themselves. In addition, the robots can also work out the target position thereof by using the rotating speed of left and right wheels or displacement information recorded by other local coordinate systems if a global coordinate system is not available. The method helps the robots to calculate the target position thereof efficiently, and provides a feasible scheme for efficient formation of multiple mobile robots.

The invention discloses a method for calibrating base coordinate systems of a multi-robot system. The method includes the steps of firstly, grouping robots pairwise according to coordination relations; secondly, establishing a constraint relationship between base coordinate systems of each pair of robots; thirdly, adopting alignment tools mounted at the tail ends of the robots, acquiring coordinate data, and enabling the system to automatically process the constraint relationships and the coordinate data acquired by the alignment tools so as to obtain a pose relationship of each pair of robots and complete calibration; finally, repeating the steps to complete calibration of the base coordinate systems of the multi-robot system. The invention further discloses equipment for calibrating the base coordinate systems of the multi-robot system. Recalibration of the multi-robot system can be completed automatically after the equipment is installed, and particularly, recalibration is necessary due to the fact that displacement is inevitable after the robots work every time. The method and the equipment for calibrating the base coordinate systems of the multi-robot system can complete calibration automatically and quickly.

The invention provides a multi-robot system and a communication system thereof. The multi-robot system comprises a working group composed of working robots and a standby working group composed of standby robots, the working or standby robots in the working group or the standby working group are the same in hardware structure and close in physical positions, and the robots in each group is physically replaceable. The client/server and end-to-end mixed communication system in explicit communication is high in the expandability, and easy to realize different application scenes; the system is also based on Ether CAT, so that instantaneity, expansibility and network bandwidth are compatible; and the robots do not communicate with one another directly, instead, all the robots communicate with a main controller. The main controller integrates feedback information of the robots with sensor data, process the data, and notifies the robots to implement different operation logics or fault recovery processes.