Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in...

Learning scalable and efficient communication policies for multi-robot collision avoidance

Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions to avoid collisions. However, the risk of collision between robots varies as they move and communication may not always be needed. This paper presents an efficient communication method that addresses the problem of “when...

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a...

Distributed Nonlinear Trajectory Optimization for Multi-Robot Motion Planning

This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an...

Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells

In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the...

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is...

Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments

We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization...

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by...

Where to go next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require...

Online trajectory planning and control of a MAV payload system in dynamic environments

Micro Aerial Vehicles (MAVs) can be used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches for path planning of MAV swung payload system either compute conservative minimal-swing trajectories or pre-generate agile collision-free...

Model predictive contouring control for collision avoidance in unstructured dynamic environments

This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive...

Sample efficient learning of path following and obstacle avoidance behavior for Quadrotors

In this letter, we propose an algorithm for the training of neural network control policies for quadrotors. The learned control policy computes control commands directly from sensor inputs and is, hence, computationally efficient. An imitation learning algorithm produces a policy that reproduces the behavior of a supervisor. The supervisor...

Distributed multi-robot formation control in dynamic environments

This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both...

Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

When several Multirotor Micro Aerial Vehicles (MAVs) share the same airspace, reliable and robust collision avoidance is required. In this paper we address the problem of multi-MAV reactive collision avoidance. We employ a model-based controller to simultaneously track a reference trajectory and avoid collisions. Moreover, to achieve a higher...