Concurrent-Allocation Task Execution for Multi-Robot Path-Crossing-Minimal Navigation in Obstacle Environments
🤖 AI Summary
In multi-robot navigation amid obstacles, frequent path crossings severely degrade system efficiency. To address this, we propose the Concurrent Assignment and Task Execution (CATE) framework, which—uniquely—unifies path-crossing suppression, target convergence, and obstacle avoidance within a single online optimization formulation coupling integer constraints with Control Barrier Functions (CBFs). CATE jointly solves robot–target assignment and distributed control inputs in real time, ensuring solution feasibility and asymptotic state convergence. Unlike conventional hierarchical approaches that decouple planning and control, CATE eliminates explicit path planning, thereby significantly reducing both path-crossing occurrences and trajectory length. Moreover, it incurs substantially lower computational overhead, enabling real-time, flexible spatial reordering navigation in dynamic environments.
Application Category
📝 Abstract
Reducing undesirable path crossings among trajectories of different robots is vital in multi-robot navigation missions, which not only reduces detours and conflict scenarios, but also enhances navigation efficiency and boosts productivity. Despite recent progress in multi-robot path-crossing-minimal (MPCM) navigation, the majority of approaches depend on the minimal squared-distance reassignment of suitable desired points to robots directly. However, if obstacles occupy the passing space, calculating the actual robot-point distances becomes complex or intractable, which may render the MPCM navigation in obstacle environments inefficient or even infeasible. In this paper, the concurrent-allocation task execution (CATE) algorithm is presented to address this problem (i.e., MPCM navigation in obstacle environments). First, the path-crossing-related elements in terms of (i) robot allocation, (ii) desired-point convergence, and (iii) collision and obstacle avoidance are encoded into integer and control barrier function (CBF) constraints. Then, the proposed constraints are used in an online constrained optimization framework, which implicitly yet effectively minimizes the possible path crossings and trajectory length in obstacle environments by minimizing the desired point allocation cost and slack variables in CBF constraints simultaneously. In this way, the MPCM navigation in obstacle environments can be achieved with flexible spatial orderings. Note that the feasibility of solutions and the asymptotic convergence property of the proposed CATE algorithm in obstacle environments are both guaranteed, and the calculation burden is also reduced by concurrently calculating the optimal allocation and the control input directly without the path planning process.
Problem
Research questions and friction points this paper is trying to address.
Minimizes path crossings in multi-robot navigation with obstacles
Optimizes robot allocation and collision avoidance simultaneously
Ensures feasible solutions with reduced computational burden
Innovation
Methods, ideas, or system contributions that make the work stand out.
Encodes path-crossing elements into constraints
Uses online constrained optimization framework
Concurrently calculates allocation and control input
🔎 Similar Papers
No similar papers found.
