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第37届中国控制会议

作者： Lv Yongshen Yang Xuerong Yang Yajun Pan Shengdong Space Engineering University

According to the problem of ugvs’ formation control in the process of motion, a formation control method of ugvs based on artificial potential field is proposed, established on the collision avoidance, aggregation and speed matching rules of ugvs. First establish the ugvs’ kinetic model in accordance to the motion rules, then design the formation control algorithm based on artificial potential field function, which is used to control the formation during the movement of ugvs. Finally, the results of simulation and physical experiment verify the effectiveness of formation control method, which is also suitable for the formation control of large-scale ugvs.

关键词： ugvs Motion rules Artificial potential field Kinetic model Formation control algorithm

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A Reference Path Guided RRT* Method for the Local Path Planning of ugvs

A Reference Path Guided RRT* Method for the Local Path Plann...

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第三十九届中国控制会议

作者： Hongju Tang Qi Zhu Erke Shang Bin Dai Chaofang Hu Tianjin University National Innovation Institute of Defense Technology

This paper proposes a novel local path planning algorithm for Unmanned Ground Vehicles(ugvs). Rapidly-exploring Random Tree Star(RRT) has recently gained immense popularity in the path planning as it provides a probabilistically complete and asymptotically optimal solution. But the uniform random sampling strategy of the traditional RRT results in blind searching and massive computation. Our algorithm introduces a reference path guided non-uniform sampling approach to improve the planning performance. When the reference path is available, the RRT based planner samples along the reference path. When there are obstacles on the reference path, guide points are generated to guide the sampling. Simulation results show that the algorithm can reduce the blindness of search, decrease the number of extended nodes, and generate smoother paths.

关键词： Path Planning RRT* Reference Path ugvs

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Hamiltonian Servo: Control and Estimation of a Large Team of Autonomous Robotic Vehicles

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Intelligent Control and Automation 2017年第4期8卷 175-197页

作者： Vladimir Ivancevic Peyam Pourbeik Joint and Operations Analysis Division Defence Science & Technology Group Canberra Australia CEWD Assured Communications Survivable Networks Defence Science & Technology Group Adelaide Australia

This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dimensional, nonlinear and non-Gaussian generalization of the classical Kalman servo system. After defining the Kalman servo as a motivation, we define the affine Hamiltonian neural network for adaptive nonlinear control of a team of ugvs in continuous time. We then define a high-dimensional Bayesian particle filter for estimation of a team of ugvs in discrete time. Finally, we formulate a hybrid Hamiltonian servo system by combining the continuous-time control and the discrete-time estimation into a coherent framework that works like a predictor-corrector system.

关键词： Team of ugvs Kalman Servo Hamiltonian Control Bayesian Estimation

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