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Sampling-based algorithms for optimal motion planning
We show that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based path planning algorithms converges almost surely to a non-optimal value. Expand
Distributed Event-Triggered Control for Multi-Agent Systems
Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agentExpand
Incremental Sampling-based Algorithms for Optimal Motion Planning
We propose a new algorithm 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. Expand
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Expand
Real-time motion planning for agile autonomous vehicles
Planning the path of an autonomous, agile vehicle in a dynamic environment in the presence of fixed and moving obstacles . Expand
Anytime Motion Planning using the RRT*
The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Expand
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency and convenience of automotive transportation. Expand
Real-Time Motion Planning With Applications to Autonomous Urban Driving
This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Expand
Maneuver-based motion planning for nonlinear systems with symmetries
In this paper, we introduce 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. Expand
Optimal kinodynamic motion planning using incremental sampling-based methods
Sampling-based algorithms such as the Rapidly-exploring Random Tree (RRT) have been recently proposed as an effective approach to computationally hard motion planning problem. Expand