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Sampling-based algorithms for optimal motion planning
TLDR
The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.
Distributed Event-Triggered Control for Multi-Agent Systems
TLDR
The controller updates considered here are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem.
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
TLDR
A more general mathematical model for real-time high-capacity ride-sharing that scales to large numbers of passengers and trips and dynamically generates optimal routes with respect to online demand and vehicle locations is presented.
Incremental Sampling-based Algorithms for Optimal Motion Planning
TLDR
A new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path returned by RRG converges to the optimum almost surely, and a tree version of RRG is introduced, called RRT∗, which preserves the asymptotic optimality ofRRG while maintaining a tree structure like RRT.
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
TLDR
The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting and to gain insight into the strengths and limitations of the reviewed approaches.
Real-Time Motion Planning With Applications to Autonomous Urban Driving
TLDR
The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.
Anytime Motion Planning using the RRT*
TLDR
This paper presents two key extensions to the RRT*, committed trajectories and branch-and-bound tree adaptation, that together enable the algorithm to make more efficient use of computation time online, resulting in an anytime algorithm for real-time implementation.
Real-time motion planning for agile autonomous vehicles
TLDR
This paper proposes a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles that addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning.
Optimal kinodynamic motion planning using incremental sampling-based methods
TLDR
It is shown that the RRT* algorithm equipped with any local steering procedure that satisfies this condition converges to an optimal solution almost surely, while maintaining the same properties of the standard RRT algorithm.
Maneuver-based motion planning for nonlinear systems with symmetries
TLDR
An approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control input.
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