Optimization-Based Collision Avoidance

Zhang, Xiaojing; Liniger, Alexander; Borrelli, Francesco

doi:10.1109/tcst.2019.2949540

Cited by 216 publications

(181 citation statements)

References 62 publications

(99 reference statements)

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“…We envision accomplishing this by, among others, more efficient heuristic trajectory pruning techniques and exploiting the parallelizability of the method. Finally, we also plan to incorporate the collision constraints in the low-level MPC problem using the method proposed in [40]. where m is the mass of the vehicle, I z is the moment of inertia, and l r and l f are the distances from the CoG to the rear and the front wheel, respectively.…”

Section: Discussionmentioning

confidence: 99%

A Noncooperative Game Approach to Autonomous Racing

Liniger

Lygeros

2020

IEEE Trans. Contr. Syst. Technol.

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We consider autonomous racing of two cars and present an approach to formulate racing decisions as a noncooperative non-zero-sum game. We design three different games where the players aim to fulfill static track constraints as well as avoid collision with each other; the latter constraint depends on the combined actions of the two players. The difference between the games are the collision constraints and the payoff. In the first game collision avoidance is only considered by the follower, and each player maximizes their own progress towards the finish line. We show that, thanks to the sequential structure of this game, equilibria can be computed through an efficient sequential maximization approach. Further, we show these actions, if feasible, are also a Stackelberg and Nash equilibrium in pure strategies of our second game where both players consider the collision constraints. The payoff of our third game is designed to promote blocking, by additionally rewarding the cars for staying ahead at the end of the horizon. We show that this changes the Stackelberg equilibrium, but has a minor influence on the Nash equilibria. For online implementation, we propose to play the games in a moving horizon fashion, and discuss two methods for guaranteeing feasibility of the resulting coupled repeated games. Finally, we study the performance of the proposed approaches in simulation for a set-up that replicates the miniature race car tested at the Automatic Control Laboratory of ETH Zürich. The simulation study shows that the presented games can successfully model different racing behaviors and generate interesting racing situations.

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Section: Discussionmentioning

confidence: 99%

A Noncooperative Game Approach to Autonomous Racing

Liniger

Lygeros

2020

IEEE Trans. Contr. Syst. Technol.

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“…This constraint formulation uses a simple (conservative) over-approximation and assumes static obstacles. Both limitations can be addressed by using the exact reformulation in [25] based on duality and using the robust extension in [26] for uncertain moving obstacles.…”

Section: A Robotic Systemmentioning

confidence: 99%

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert

Köhler

Berenz

et al. 2020

IEEE Robot. Autom. Lett.

132

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Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

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“…X free (t) is often non-convex and the OCP in (3.2) is therefore a combinatorial problem with possibly different classes of solution trajectories. A proper initial guess is therefore required to find a good locally optimal (or even a feasible) solution to the OCP in (3.2) (Zhang et al, 2017).…”

Section: Problem Formulationmentioning

confidence: 99%

On motion planning and control for truck and trailer systems

Ljungqvist

2019

Linköping Studies in Science and Technology. Licentiate Thesis

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This is a Swedish Licentiate's Thesis. Swedish postgraduate education leads to a Doctor's degree and/or a Licentiate's degree. A Doctor's Degree comprises 240 ECTS credits (4 years of full-time studies). A Licentiate's degree comprises 120 ECTS credits, of which at least 60 ECTS credits constitute a Licentiate's thesis. Linköping studies in science and technology. Licentiate Thesis No. 1832 On motion planning and control for truck and trailer systems

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Optimization-Based Collision Avoidance

Cited by 216 publications

References 62 publications

A Noncooperative Game Approach to Autonomous Racing

A Noncooperative Game Approach to Autonomous Racing

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

On motion planning and control for truck and trailer systems

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