Probabilistic motion planning for non-Euclidean and multi-vehicle problems

Trajectory planning tasks for non-holonomic or collaborative systems are naturally modeled by state spaces with non-Euclidean metrics. However, existing proofs of convergence for sample-based motion planners only consider the setting of Euclidean state spaces. We resolve this issue by formulating a flexible framework and set of assumptions for which the widely-used PRM*, RRT, and RRT* algorithms remain asymptotically optimal in the non-Euclidean setting. The framework is compatible with collaborative trajectory planning: given a fleet of robotic systems that individually satisfy our assumptions, we show that the corresponding collaborative system again satisfies the assumptions and therefore has guaranteed convergence for the trajectory-finding methods. Our joint state space construction builds in a coupling parameter $1\leq p\leq \infty$, which interpolates between a preference for minimizing total energy at one extreme and a preference for minimizing the travel time at the opposite extreme. We illustrate our theory with trajectory planning for simple coupled systems, fleets of Reeds-Shepp vehicles, and a highly non-Euclidean fractal space.Comment: 12 pages, 8 figures. Substantial revision

A Survey and Analysis of Multi-Robot Coordination

International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings

Safe, Scalable, and Complete Motion Planning of Large Teams of Interchangeable Robots

Large teams of mobile robots have an unprecedented potential to assist humans in a number of roles ranging from humanitarian efforts to e-commerce order fulfillment. Utilizing a team of robots provides an inherent parallelism in computation and task completion while providing redundancy to isolated robot failures. Whether a mission requires all robots to stay close to each other in a formation, navigate to a preselected set of goal locations, or to actively try to spread out to gain as much information as possible, the team must be able to successfully navigate the robots to desired locations. While there is a rich literature on motion planning for teams of robots, the problem is sufficiently challenging that in general all methods trade off one of the following properties: completeness, computational scalability, safety, or optimality. This dissertation proposes robot interchangeability as an additional trade-off consideration. Specifically, the work presented here leverages the total interchangeability of robots and develops a series of novel, complete, computationally tractable algorithms to control a team of robots and avoid collisions while retaining a notion of optimality. This dissertation begins by presenting a robust decentralized formation control algorithm for control of robots operating in tight proximity to one another. Next, a series of complete, computationally tractable multiple robot planning algorithms are presented. These planners preserve optimality, completeness, and computationally tractability by leveraging robot interchangeability. Finally, a polynomial time approximation algorithm is proposed that routes teams of robots to visit a large number of specified locations while bounding the suboptimality of total mission completion time. Each algorithm is verified in simulation and when applicable, on a team of dynamic aerial robots

Subdimensional Expansion and Optimal Task Reassignment

Multirobot path planning and task assignment are traditionally treated separately, however task assignment can greatly impact the difficulty of the path planning problem, and the ultimate quality of solution is dependent upon both. We introduce task reassignment, an approach to optimally solving the coupled task assignment and path planning problems. We show that task reassignment improves solution quality, and reduces planning time in some situations