This paper investigates a distributed trajectory optimization and control problem for a collection of vehicles with a quadratic spacing policy. A quadratic spacing policy is introduced based on the expected platoon sp...
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This paper investigates a distributed trajectory optimization and control problem for a collection of vehicles with a quadratic spacing policy. A quadratic spacing policy is introduced based on the expected platoon speed to improve flexibility of speed planning and regulation. The trajectoryoptimization problem is solved using distributed convex optimization based on spacing errors minimization, resulting in an algorithm to provide the optimal trajectory for all following vehicles. Then, a PID-type sliding mode controller with a double high-power reaching law is presented for speed tracking control of each follower. The methodology can guarantee the internal stability, string stability and traffic flow stability with ignorable turbulence of spacing and speed, as demonstrated by numerical simulations.
This paper presents a distributed algorithm for the minimum-time reconfiguration of multi-agent formation. The reconfiguration problem is formulated as an optimal control problem where the manifold of terminal states ...
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ISBN:
(纸本)9781665440899
This paper presents a distributed algorithm for the minimum-time reconfiguration of multi-agent formation. The reconfiguration problem is formulated as an optimal control problem where the manifold of terminal states includes parameters to be optimized. The proposed algorithm lays its foundation on the control vector parametrization (CVP) method, the sequential convex programming (SCP) method, and the distributed alternating direction method of multipliers (D-ADMM). By using the CVP method, the original optimal control problem is firstly transcribed into a nonlinear programming problem, which can then be solved by the SCP method. To achieve the distributed implementation the SCP method is integrated with D-ADMM. The effectiveness of the algorithm is demonstrated in a case study of the time-optimal formation reconfiguration of four satellites. A comparison with the global optimization technique shows that the algorithm outputs the global minimum for the studied case.
This paper presents a distributed algorithm for the minimum-time reconfiguration of multi-agent *** reconfiguration problem is formulated as an optimal control problem where the manifold of terminal states includes pa...
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This paper presents a distributed algorithm for the minimum-time reconfiguration of multi-agent *** reconfiguration problem is formulated as an optimal control problem where the manifold of terminal states includes parameters to be *** proposed algorithm lays its foundation on the control vector parametrization(CVP)method,the sequential convex programming(SCP) method,and the distributed alternating direction method of multipliers(D-ADMM).By using the CVP method,the original optimal control problem is firstly transcribed into a nonlinear programming problem,which can then be solved by the SCP *** achieve the distributed implementation the SCP method is integrated with *** effectiveness of the algorithm is demonstrated in a case study of the time-optimal formation reconfiguration of four satellites.A comparison with the global optimization technique shows that the algorithm outputs the global minimum for the studied case.
Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To b...
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Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the global planning and local trajectoryoptimizations. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.
Multi-robot learning has been extensively studied recently. Developing provably-correct algorithms for learning decentralized control policies remains challenging. In this letter, we propose a sample-efficient multi-r...
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Multi-robot learning has been extensively studied recently. Developing provably-correct algorithms for learning decentralized control policies remains challenging. In this letter, we propose a sample-efficient multi-robot learning method based on guided policy search to learn decentralized swarm control policies. The proposed method uses distributed trajectory optimization to provide guiding trajectory samples for policy training. In turn, the learned policy is exploited to update the trajectoryoptimization results so that the guiding trajectories are reproducible by the current policy. A learning algorithm is designed to alternate between distributed trajectory optimization and policy optimization, which eventually converges to a solution with good long-term performance. We demonstrate the effectiveness of our method in a multi-robot rendezvous problem. The simulation results in a robot simulator show that our method efficiently learn decentralized control policy with substantially less training samples.
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