Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation. In this letter, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments. For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form. This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state. Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints. We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance. Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality.

I. Introduction

With the advancement of autonomous navigation technology for car-like robots, some scenes depicted in science fiction movies, such as adaptive and flexible collaborative rescue and transportation in complex environments, have become a realistic possibility [1], [2]. The key to realizing these tasks lies in achieving precise and computing-efficient collaborative planning for the car-like robotic team, which is a high-dimensional, strongly-coupled system with non-holonomic motion constraints. However, achieving both high precision and high efficiency can be a dilemma, remaining challenging to perform online adaptive and precise collaborative trajectory planning in unstructured environments.