An Efficient Algorithm for Optimal Trajectory Generation for Heterogeneous Multi-Agent Systems in Non-Convex Environments

Robinson, DP; Mar, Robert T.; Estabridis, Katia; Hewer, Gary A.

doi:10.1109/lra.2018.2794582

Cited by 45 publications

(28 citation statements)

References 15 publications

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“…If no collisions are detected, we solve the reduced problem in (21), otherwise we solve the collision avoidance problem in (29). If the optimizer finds a solution to the QP, then we can propagate the states using (7) and obtain the predicted position and velocity over the horizon (lines [6][7][8][9]. Lastly, if a solution for the transition was found, we interpolate the solution with time step T s to obtain a higher resolution trajectory.…”

Section: The Algorithmmentioning

confidence: 99%

Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

Luis

Schoellig

2019

IEEE Robot. Autom. Lett.

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This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control (DMPC). Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbours, the agents are able to detect and avoid collisions while moving towards their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming (SCP), while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.

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Section: The Algorithmmentioning

confidence: 99%

Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

Luis

Schoellig

2019

IEEE Robot. Autom. Lett.

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“…Recently, the consensus of heterogeneous MASs has gained much attention 20,23‐34 . In Reference 20, the linear and saturated controllers are designed, respectively, to achieve the consensus of heterogeneous MASs under undirected connected graphs.…”

Section: Introductionmentioning

confidence: 99%

Mean square and almost sure consensus of heterogeneous multi‐agent systems with multiplicative noise

Zhang

Zong

et al. 2021

Intl J Robust & Nonlinear

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This paper studies the consensus problem of heterogeneous multi‐agent systems (MASs) composed of first‐order and second‐order agents. To describe the communication environment realistically, the effect of the multiplicative noise on the communication network is considered. Firstly, the consensus problem is transformed into the stability problem of stochastic differential equations with multiplicative noise. Then, a stability analysis method for such stochastic differential equations is proposed by structuring the appropriate Lyapunov function. Some sufficient conditions for both mean square (m.s.) and almost sure (a.s.) consensus are obtained for leader‐free/leader‐following heterogeneous MASs. These sufficient conditions, expressed by simple scalar inequalities, are explicitly related to control gains, noise intensities, and network topology. Furthermore, the m.s. and a.s. convergence rates of the consensus protocol are also obtained. Finally, numerical simulation shows the effectiveness of our theoretical results.

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“…Real-time collision-free motion planning and control for autonomous vehicles have received a considerable amount of attentions, and share many research methods with robotics literature (LaValle 2006;González et al 2016;Nilsson et al 2016;Rasekhipour et al 2016;Ye et al 2018;Vorobieva et al 2015;Du and Tan 2015;Upadhyay and Ratnoo 2018;Muller et al 2007;Xu et al 2018;Dolgov et al 2010;Likhachev and Ferguson 2009;Tazaki et al 2017;Liu et al 2017;Robinson et al 2018;Li and Shao 2015;Li et al 2016;Zips et al 2016;Khatib 1986;Oetiker et al 2009). Typically, autonomous parking is a critical maneuver especially in big narrow cities.…”

Section: Introductionmentioning

confidence: 99%

“…And the application challenges lie in how to represent the geometry collision-free constraints effectively. The point-point distance of circles in Robinson et al (2018) and area criterion of rectangles in Li and Shao (2015) are both straightforward collision avoidance methods. However, circle bounding volume approximation is too conservative to realize motion in complex and high-precision scenarios.…”

Section: Introductionmentioning

confidence: 99%

A bilevel optimal motion planning (BOMP) model with application to autonomous parking

Shi

Xiong

Chen

et al. 2019

Int J Intell Robot Appl

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In this paper, we present a bilevel optimal motion planning (BOMP) model for autonomous parking. The BOMP model treats motion planning as an optimal control problem, in which the upper level is designed for vehicle nonlinear dynamics, and the lower level is for geometry collision-free constraints. The significant feature of the BOMP model is that the lower level is a linear programming problem that serves as a constraint for the upper-level problem. That is, an optimal control problem contains an embedded optimization problem as constraints. Traditional optimal control methods cannot solve the BOMP problem directly. Therefore, the modified approximate Karush-Kuhn-Tucker theory is applied to generate a general nonlinear optimal control problem. Then the pseudospectral optimal control method solves the converted problem. Particularly, the lower level is the J 2-function that acts as a distance function between convex polyhedron objects. Polyhedrons can approximate objects in higher precision than spheres or ellipsoids. As a result, a fast high-precision BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed. Simulation results and experiment on Turtlebot3 validate the BOMP model, and demonstrate that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method. Keywords Autonomous parking • Optimal control • Bilevel optimal motion planning (BOMP) • J 2-function * Youlun Xiong

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An Efficient Algorithm for Optimal Trajectory Generation for Heterogeneous Multi-Agent Systems in Non-Convex Environments

Cited by 45 publications

References 15 publications

Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

Mean square and almost sure consensus of heterogeneous multi‐agent systems with multiplicative noise

A bilevel optimal motion planning (BOMP) model with application to autonomous parking

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