Scalable time-constrained planning of multi-robot systems

Abstract

This paper presents a scalable procedure for time-constrained planning of a class of uncertain nonlinear multi-robot systems. In particular, we consider N robotic agents operating in a workspace which contains regions of interest (RoI), in which atomic propositions for each robot are assigned. The main goal is to design decentralized and robust control laws so that each robot meets an individual high-level specification given as a metric interval temporal logic (MITL), while using only local information based on a limited sensing radius. Furthermore, the robots need to fulfill certain desired transient constraints such as collision avoidance between them. The controllers, which guarantee the transition between regions, consist of two terms: a nominal control input, which is computed online and is the solution of a decentralized finite-horizon optimal control problem (DFHOCP); and an additive state feedback law which is computed offline and guarantees that the real trajectories of the system will belong to a hyper-tube centered along the nominal trajectory. The controllers serve as actions for the individual weighted transition system (WTS) of each robot, and the time duration required for the transition between regions is modeled by a weight. The DFHOCP is solved at every sampling time by each robot and then necessary information is exchanged between neighboring robots. The proposed approach is scalable since it does not require a product computation among the WTS of the robots. The proposed framework is experimentally tested and the results show that the proposed framework is promising for solving real-life robotic as well as industrial applications.

Theoretical Framework and Practical Considerations for Achieving Superior Multi-Robot Exploration: Hybrid Cheetah Optimization with Intelligent Initial Configurations

“…To address these challenges, recent algorithms ensure feasible trajectories under specific initial conditions [49]. These are pivotal in multi-robot systems, emphasizing collision prevention and coordination.…”

Decentralized Multi-agent Coordination under MITL Specifications and Communication Constraints

“…For the sake of clarity, we use a gridworld in the examples, but our approach applies to any WTS, obtained e.g. through triangulation [20] or other cell partitioning methods [19], discrete abstraction of a robot's dynamics, and estimate of travel time of between the cells.…”

“…During the execution step, R 0 implements two rounds of pursuitevasion strategy in the workspace to exchange all requests while R i , i ∈ [1, N ] executes their timed runs in parallel independently (line 9-10). Robots R i , i ∈ [1, N ] then update their tasks and proceed with execution (line [11][12][13][14][15][16][17][18][19] If there exists N timed runs ρ i |= ϕ ′ i ∧ j̸ =i ϕ j,i , ∀i ∈ [1, N ], the joint run satisfying all mission specifications φ i , ∀i ∈ [1, N ] is found (line 20).…”

“…However, there are no explicit time constraints for the tasks. [19] proposed a solution similar to ours for decentralized multi-agent coordination with time and communication constraints, but our method only needs to communicate twice within the sensing radius per agent rather than after each sampling time and can perform more complex and diverse cooperative tasks than navigation and collision avoidance.…”

Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

“…Multi-robot hunting is one of the most important and complex challenges in the field of multi-robot cooperation due to the fact that it includes all multi-robot system subproblems such as task allocation, target localization, collaborative pursuing, obstacle and collisions avoidance. The research on multi-robot system cooperative hunting covers many disciplines and domain knowledge, such as optimization algorithms [8], real-time dynamic path planning [9]- [10], multi-robot coordination [11]- [13], planning and learning [14], and communication [15].…”