Real-Time Optimized Rendezvous on Nonholonomic Resource-Constrained Robots

Abstract

In this work, we consider a group of differential-wheeled robots endowed with noisy relative positioning capabilities. We develop a decentralized approach based on a receding horizon controller to generate, in real-time, trajectories that guarantee the convergence of our robots to a common location (i.e. rendezvous). Our receding horizon controller is tailored around two numerical optimization methods: the hybrid-state A* and trust-region algorithms. To validate both methods and test their robustness to computational delays, we perform exhaustive experiments on a team of four real mobile robots equipped with relative positioning hardware.

Position centroid rendezvous and centroid formation of multiple unicycle agents

“…For system (15), since the graph satisfies Assumption 1, it follows from Lemma 1 that (x,ȳ) globally exponentially converge to ((1/n) n i=1x i (0), (1/n) n i=1ȳ i (0)) with the rate λ 2L , respectively. Notice thatp i (0) = p i (0), hencep i globally exponentially tends to p cen (0) with the rate λ 2L .…”

“…The rendezvous/formation control problems of multiple unicycle agents have been studied extensively in many literatures, which are mutually convertible in most cases. The existing control laws have two categories: 'leader-following control laws' [1,2,[13][14][15][16][17] and 'non-leader ones' [18][19][20][21][22][23][24][25][26][27][28]. The leader-following control laws are designed such that the entire/portion states of each agent track the reference trajectory, sometimes with a desired team shape.…”

“…Using dynamic feedback linearisation and small-gain techniques, Liu and Jiang [1] proposed a leader-following formation scheme for unicycle robots without assuming any tree sensing structures, which is also robust to measurement errors. The receding horizon control method was used in [15,16] to solve the real-time and optimal position rendezvous control problem for non-holonomic robots with positioning noises, and was employed in [17] to solve the tracking, regulation and formation problems for the entire state of non-holonomic vehicles simultaneously. In [19], Yu et al solved the practical position rendezvous problem of non-holonomic agents using quantised angular velocity controller only.…”

The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox

“…Released in 2006, the Khepera III has seen over 600 sales to 150 universities worldwide and has been used in hundreds of publications. In our lab, it has been successfully employed across diverse research topics, including odor sensing [17], navigation and localization [13], formation control [6], flocking [12], and learning [3], as well as numerous student projects.…”