63 results about "Swarm robotics" patented technology

The present invention provides a swarm of robots and a related method of operating the swarm. The robots are programmed to start at a nest and to select a dispersal direction from a uniform random distribution. The robots travel along the dispersal direction until transitioning to a search mode upon reaching a search site, where the robot performs a correlated random walk with fixed step size and direction and using a standard deviation to determine how correlated the direction of the next step of the robot is with the direction of the previous step. If no resource is found within predetermined time t independently determined by each of said robots, the robot returns to the nest and repeats the above steps.

A group robot cooperation search method based on an improved particle swarm algorithm belongs to the group robot technology field and is used to improve problems that an exploration range is too concentrated and a remote area is easy to omit. The method comprises the following steps of (1) initializing a robot system state; (2) using an artificial potential field improvement strategy to carry outroaming search and obtaining the rough position of a target; (3) detecting a target signal by a robot, and then determining whether to obtain a target search right according to a result, and decidingwhether to continue the roaming search or enter a cooperation search phase; (4) adopting the improved particle swarm algorithm by a robot group searching for a same target, carrying out fine search onthe target and improving search efficiency; and (5) discovering all the targets by roaming robots, and carrying out fine positioning through the particle swarm algorithm and ending the task. In the method, the task can be effectively completed during multi-robot cooperation target search; and a PSO algorithm based on an assisted orientation technology can be used to improve the convergence efficiency of the later period of the particle swarm algorithm.

A system for localizing a swarm of robotic platforms utilizing ranging sensors. The swarm is localized by purposely leaving some of the platforms of the swarm stationary, providing localization to the moving ones. The platforms in the swarm can alternate between a stationary and moving state.

The invention relates to a microrobot and a swarm robot system comprising multiple microrobots. The swarm robot system comprises multiple microrobot monomers with the same structures, and a test platform for test activities and charging of the multiple microrobot monomers; and the test platform is provided with a platform body, and a wireless charge emitting coil array arranged on the lower surface of the platform body. Each microrobot is provided with a driving part for driving the robot monomers to move; a control part for controlling interaction behaviors of the robot monomers and comprising an upper circuit board, a middle circuit board, two side circuit boards and a lower circuit board arranged in sequence from top to bottom and in mutual circuit connection; and a power supply part for supplying electricity to the driving part and the control part and comprising a battery and a wireless charge coil matched with the wireless charge emitting coil array. The microrobot and the swarm robot system have the characteristics of convenience in movement, delicate structure and stable charge.

The invention discloses a multi-target capturing method for cooperative operation of swarm robots in a complex non-convex environment. Firstly, a motion model of multiple targets and dynamic obstaclesin a complex non-convex environment is designed; then, the capturing behavior under the complex environment is studied; a multi-target simplified virtual stress model is constructed; based on the stress model, a dynamic multi-target self-organizing task allocation method and a specific process of collaborative self-organizing dynamic multi-target capturing are provided. According to the process multi-target task self-organizing allocation method, one target can be allocated to each robot only based on the two nearest neighbor task information; and then, in a complex non-convex environment, each robot can avoid various obstacles through a multi-target obstacle tracking algorithm based on the distance between the robot and the obstacle, calculation is simple and efficient, implementation iseasy, and the whole method process has good obstacle avoidance performance, robustness, expandability and flexibility.

The invention discloses a swarm robot multi-target searching method in an unknown environment. The swarm robot multi-target searching method comprises the following steps that an unknown environment model is constructed; a robot detects a target signal, dynamic division is carried out based on a target response function, and a set where robots completing the same sub-task are located forms a sub-alliance; closed-loop regulation is introduced, resource allocation of each sub-task is evaluated, and a new sub-alliance is formed; robots which do not form the sub-alliance perform roaming search; and the robots forming the sub-alliance search a target based on a particle swarm algorithm of position estimation and an obstacle avoidance strategy of boundary scanning. By means of the swarm robot multi-target searching method in the unknown environment, according to the obstacle avoidance strategy of boundary scanning, the distance and angle relation between the nearest two points and the boundary points and the robots is used for obstacle avoidance; and according to the particle swarm algorithm of position estimation, the approximate position of the target point can be deduced by using theavailable target signal, the particle swarm algorithm is matched, the target position is quickly reached, so that the path and the searching time during target searching are reduced.

The invention belongs to the robot field and discloses a cluster intelligent control system, which comprises a host computer, a WIFI module, a microcontroller, a strain gauge, a motor, a gyroscope and spiral fan blades, wherein the strain gauge is used for measuring the thrust force value exerted by each fan on an individual robot and transmits the value to the microcontroller, the microcontroller, based on the comparison between the obtained actual thrust value and the target thrust value, adjusts the motor speed in real time; and at the same time, the microcontroller obtains the current angular velocity of the individual robot through the gyroscope so as to obtain the current attitude of the individual robot and to adjust the rotational speed difference of the motor in real time. In doing so, the individual robot in a cluster of robots can move according to a set track. According to the invention, it is possible to adjust and control the actions of a cluster of robots in real time according to the requirements, and to optimize the motion tracks of the cluster of robots by using the mixed particle swarm gravitation algorithm.

The invention provides a distributed group robot cooperative clustering algorithm based on an improved gene regulation network. Through embedding a diamond mesh distribution equation and a trajectoryfollowing equation in a gene regulation network model based on the Turing reaction diffusion mechanism, the moving vector speed of each robot is controlled, thus each robot whose initial state is in random distribution always gathers at a preset cluster trajectory position at a time t, the robots are arranged in a self-organized way to be in a diamond mesh distribution and can avoid obstacles in adynamic environment and repair formation by themselves. Parameter values in the improved gene regulation network are given by an NSGA II optimization algorithm. The distributed group robot cooperative clustering algorithm has the advantages of low computational complexity and good expansibility, for any robot, only the collection of the location information of a neighboring robot is needed, so arequired communication range is small, and the communication burden is effectively reduced. In addition, if some robots fail in operation, a system can still work normally, and the algorithm has goodrobustness and a great application prospect.

The invention discloses a swarm robot multi-target search method based on unknown environment collision conflict prediction. The method comprises the following steps: constructing an unknown environment model and a target response function; the robot detects a target signal and performs dynamic task division to form a sub-alliance; introducing closed-loop regulation to reform a new sub-alliance; the robots not forming the sub-alliance perform roaming search, and the robots forming the sub-alliance perform coordinated search; in the roaming search and coordination search process, an obstacle avoidance method combining a collision expansion geometric cone and a speed obstacle method is adopted for obstacle avoidance; if the distance between the robot and the target is smaller than the threshold value, target searching is successful, and target searching is stopped; and if all the targets are successfully searched, ending the task, otherwise, continuing to search the targets until all the target search tasks are completed. According to the method, the obstacle avoidance method combining the collision expansion geometric cone and the speed obstacle method is adopted for obstacle avoidance, and the task searching time, the obstacle avoidance frequency and the energy consumption of the swarm robot are reduced.

The invention provides a swarm robot distributed competition cooperation method for multi-target tracking. The swarm robot distributed competition cooperation method comprises the following steps: 1)distributing robots and detecting position information and environment information of a target group, a coverage area of swarm robots, consumed communication power and the like in real time; 2) for each robot, acquiring information mastered by the robot communicated with the robot in a communication topological graph in real time; 3) constructing a decision scheme for optimal robot selection in swarm robot distributed competition cooperation in real time according to the information, and determining a driving information instruction for allocating the robots according to a quadratic programming method; and 4) selecting an optimal robot according to the driving information instruction, and identifying a motion coordinate system of the robot and the target group in real time to realize distributed competition cooperation of the swarm robots for multi-target task execution. According to the method, the application scene of the swarm robot is broadened by constructing a competitive cooperation mechanism, the communication load and loss are reduced through distributed design, and the stability of a swarm robot system is guaranteed.

The invention relates to the technical field of robots, more particularly to a cooperative motion method for a robot cluster in an obstacle scene. The method provided by the invention is used for enabling multiple robots to realize formation transformation, dynamic and static obstacle avoidance and cooperative motion in the obstacle scene. The method defines the cooperative control of the robot cluster in a complex environment as a distributed model predictive control problem. Each robot solves an optimization equation independently based on the information of local neighbors, which has betterrobustness and flexibility. At the same time, the method proposes the concept of a formation diagram, and uses a directed diagram to represent the robot formation. Under the premise of ensuring the connectivity of the diagram, local transformation of the desired formation can be realized by changing the edges and weights of the formation, and at the same time, the connectivity and the flexibilityof the cluster can be ensured. Besides, the method provided by the invention has a hierarchical structure. By adopting the hierarchical structure, the coupling between modules can be reduced, and algorithm design and expansion can be facilitated.