We consider the problem of coordinating a collection of robots at an intersection area taking into account dynamical constraints due to actuator limitations. We adopt the coordination space approach, which is standard in multiple robot motion planning. Assuming the priorities between robots are assigned in advance and the existence of a collision-free trajectory respecting those priorities, we propose a provably safe trajectory planner satisfying kinodynamic constraints. The algorithm is shown to run in real time and to return safe (collision-free) trajectories. Simulation results on synthetic data illustrate the benefits of the approach.
As first introduced in @cite_3 @cite_1 , the path-velocity decomposition enables to introduce an abstract space: the coordination space. It is a standard approach to robot motion planning @cite_0 @cite_6 , and the motion planning problem in the real space boils down to finding an optimal trajectory in the coordination space that is collision-free with respect to an obstacle region. The coordination space is a @math -dimensional space (where @math denotes the number of robots in the intersection) and the obstacle-region has a cylindrical structure @cite_15 . In @cite_8 , we have revisited the notion of priorities to propose a novel framework for automated intersection management based on priority assignment. It is a very intuitive notion: the priority graph indicates the relative order of robots. Our framework enables to decompose the motion planning problem problem in the coordination space into a combinatorial problem: priority assignment, and a continuous problem: finding an efficient trajectory with assigned priorities.
@cite_8 @cite_1 @cite_6 @cite_3 @cite_0 @cite_15
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Given a collection of robots sharing a common environment, assume that each possesses a graph (a one-dimensional complex also known as a roadmap) approximating its configuration space and, furthermore, that each robot wishes to travel to a goal while optimizing elapsed time. We consider vector-valued (or Pareto) optima for collision-free coordination on the product of these roadmaps with collision-type obstacles. Such optima are by no means unique: in fact, continua of Pareto optimal coordinations are possible. We prove a finite bound on the number of optimal coordinations in the physically relevant case where all obstacles are cylindrical (i.e., defined by pairwise collisions). The proofs rely crucially on perspectives from geometric group theory and CAT(0) geometry. In particular, the finiteness bound depends on the fact that the associated coordination space is devoid of positive curvature. We also demonstrate that the finiteness bound holds for systems with moving obstacles following known trajectories. We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. In simulation, we demonstrate that our methods are faster and significantly more invariant to problem algorithm parameters than existing techniques. In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A sup * -search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments. This contribution presents a novel approach for efficient online planning of topologically distinctive mobile robot trajectories. Trajectory optimization deforms an initial coarse path drafted by a global planner with respect to robot motion related objectives and constraints. The primary objective is to reach a goal state in minimal time on a collision free path that adheres to the kinematic and dynamic constraints of the mobile robot. Conventional local planners get often stuck in a local optimal trajectory as they are unable to transit across obstacles. Our approach seeks the globally optimal trajectory as it maintains and optimizes a subset of admissible candidate trajectories of distinctive topologies in parallel. In case of dynamic environments the planner switches to the current globally optimal trajectory among the candidate set. The online trajectory planning with timed elastic bands is tightly integrated with the robot motion feedback control. The comparative analysis with conventional local planners confirms the advantages of maintaining distinctive topologies to circumnavigate dynamic obstacles.