37,405 research outputs found
Recommended from our members
A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Cooperative task assignment for multiple vehicles
Multi-vehicle systems have been increasingly exploited to accomplish difficult and complex missions, where effective and efficient coordinations of the vehicles can greatly improve the team's performance. Motivated by need from practice, we study the multi-vehicle task assignment in various challenging environments. We first investigate the task assignment for multiple vehicles in a time-invariant drift field. The objective is to employ the vehicles to visit a set of target locations in the drift field while trying to minimize the vehicles' total travel time. Using optimal control theory, a path planning algorithm is designed to generate the time-optimal path for a vehicle to travel between any two prescribed locations in a drift field. The path planning algorithm provides the cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Using tools from graph theory, a lower bound on the optimal solution is found, which can be used to measure the proximity of a solution from the optimal. We propose several clustering-based task assignment algorithms in which two of them guarantee that all the target locations will be visited within a computable maximal travel time, which is at most twice of the optimal when the cost matrix is symmetric. In addition, we extend the multi-vehicle task assignment study in a time-invariant drift field with obstacles. The vehicles have different capabilities, and each kind of vehicles need to visit a certain type of target locations; each target location might have the demand to be visited more than once by different kinds of vehicles. A path planning method has been designed to enable the vehicles to move between two prescribed locations in a drift field with the minimal time while avoiding obstacles. This task assignment problem is shown to be NP-hard, and a distributed task assignment algorithm has been designed, which can achieve near-optimal solutions to the task assignment problem. Furthermore, we study the task assignment problem in which multiple dispersed heterogeneous vehicles with limited communication range need to visit a set of target locations while trying to minimize the vehicles' total travel distance. Each vehicle initially has the position information of all the targets and of those vehicles that are within its limited communication range, and each target demands a vehicle with some specified capability to visit it. We design a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected vehicle subnetwork. Then, the algorithm constructs conflict-free target assignments for the communication-connected vehicles, and guarantees that the total travel distance of the vehicles is at most twice of the optimal when the communication network is initially connected. In the end we exploit the precedence-constrained task assignment problem for a truck and a micro drone to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. The truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Integrating with a topological sorting technique, several heuristic task assignment algorithms are constructed to solve the task assignment problem
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
- …