Obstacle avoidance for multi-agent systems based on stream function and hierarchical associations
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Obstacle avoidance
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In this paper, the design of an fuzzy intelligent coordination algorithm is introduced for a team of multi mobile robots (MMR) that are confronted with obstacles. As well as obstacle avoidance, the controllers work to make the robots arrive concurrently at their target points by adjusting each of the robot's velocities as they move along their desired paths. The simulation results of three mobile robots traveling on different paths in unknown environments are presented to show the accuracy of obtaining control, coordination and obstacle avoidance by using the designed fuzzy algorithm.
Obstacle avoidance
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This paper mainly concentrates on obstacle avoidance of multi robot system in 2-D environment. An efficient and practical collision-avoidance mechanism is developed for multi agent system. The collision-avoidance mechanism is used in combination with a cooperation mechanism to accomplish an complicated task. Based on the problem of "the tower of babel", some obstacles are added in the environment, the agents must reach their destination without colliding with the obstacles and other agents. A typical kind of multi robot system is studied in this paper, and problem of collision-avoidance in multi robot system is discussed. Our purpose is to study a practical and efficient obstacle avoidance method under central control mode, which has been proved effective through simulation experiments results, and this obstacle avoidance method can solve the problem of "deadlock" to a satisfying extent.
Obstacle avoidance
Collision avoidance system
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Aiming at the problem of obstacle avoidance for mobile robot with the platform of AS-R mobile robot,a novel approach based on behavior for dynamic obstacle avoidance was put forward,which divides the robot behavior during the entire run into four behaviors,including tendency to target behaviors,obstacle avoidance behavior,going along the wall and emergency obstacle avoidance behavior of four acts,including obstacle avoidance behavior.A multi-sensor-based autonomous mobile robot obstacle avoidance mechanism was designed.A recursive median filter-based method was proposed to avoid crosstalk by grouping sampling or interval sampling technologys.The data was improved in both space and time continuity.The ultrasonic signal crosstalk and other interference random signals were effectively reduced.Experiments based on both simulation platform and AS-R robot verified the effectiveness of the proposed approach.
Obstacle avoidance
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Fuzzy control has a good ability for systems of which mathematical modeling is difficult. It also has flexible ability for control. However, when the number of the parameters are large, the number of rules increases in proportion to the square of the number of parameters.In this paper, we propose a way of decreasing the number of fuzzy rules by classifying and combining them for a mobile robot. Firstly, steering control input and velocity control input are decided separately. Secondly, those control inputs are composed with keeping the performance of control good. Finally, we show the effectiveness of the proposed method by simulations.
Obstacle avoidance
Hierarchical control system
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In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber's multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.
Flocking (texture)
Obstacle avoidance
Maxima and minima
Consensus algorithm
Swarm intelligence
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The problems of flocking with both connectivity maintenance and obstacle avoidance for the network of dynamic agents are addressed. In the case where the initial network is connected, a decentralized flocking control protocol is proposed to enable the group to asymptotically achieve the desired stable flocking motion using artificial potential functions combined with stream functions, which could not only maintain the network connectivity of the dynamic multi-agent systems for all time but also make all the agents avoid obstacles smoothly without trapping into local minima. Finally, nontrivial simulations and experiments are worked out to verify the effectiveness of the theoretical methods.
Flocking (texture)
Obstacle avoidance
Maxima and minima
Autonomous agent
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High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multiagent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multiagent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multiagent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.
Obstacle avoidance
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Vehicle collision avoidance is a promising safety approach to new transportation systems, with innovative capabilities, such as obstacle detection, vehicle collision avoidance control strategy and adaptability to different obstacles. This paper presents a Reactive Multi-agent solution to the vehicle collision avoidance control problem with a linear configuration. In our case, vehicle collision avoidance is designed as a reactive multi-agent system in which agents interact with other agents and the obstacles situated in the environment by using physics inspired behaviors. Collision avoidance stability emerges as a global result of the individual interacted agents. Vehicle avoidance control strategy stems from calculating the trajectories of the vehicle based on the decision process of the reactive multi-agent system. Furthermore, the adaptation to different kind of obstacles is made by tuning model's physical parameters. In order to assert the transition from abstract to concrete, simulations experiments have been implemented and simulations results are analyzed.
Obstacle avoidance
Adaptability
Collision avoidance system
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Obstacle avoidance
Model Predictive Control
Tracking (education)
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Guaranteed collision avoidance control laws for multi-agent systems typically rely on constant detection regions. This constraint tends to generate conservative and slower agents' trajectories. To reduce the conservatism of avoidance control laws, this letter presents two decentralized, cooperative strategies for arbitrarily large groups of agents that decrease the vehicles' effective detection regions by using velocity information. The vehicles are modeled as a class of nonlinear Lagrangian systems which full state vector represents absolute position. The control laws are proven to guarantee collision avoidance at all times and are shown to be more energy-efficient and to generate faster and smoother trajectories than traditional methods. Moreover, by decreasing the detection regions, the agents are able to converge to destinations closer to each other's avoidance regions, a feature not possible with traditional avoidance control.
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