Members of a swarm robotic system may be burdened with sensing limitations due to their operating environment and/or due to miniaturization of their onboard hardware. In the face of such sensing limitations, swarm robots may be unable to individually localize and achieve accurate motion control. This would result in the accumulation of motion errors, divergence of the swarm from its desired motion, or even a complete failure to accomplish the multi-task mission at hand. Challenges associated with swarm robots’ sensing limitations can, however, be addressed through collaborative motion control strategies. Such strategies navigate the robots to their destinations while allowing them to get assistance from their neighbors, through sensing and wireless communication, for accurate localization and motion control. While these strategies enable the robots to compensate for their individual sensing limitations, they would require them to remain in close proximity to each other. Thus, when planning their motion for a multi-task mission at hand, the connectivity constraints imposed by an adopted collaborative motion control strategy must be considered.

This Thesis presents novel strategies and methodologies applicable to robotic swarms comprising members with sensing limitations. Specifically, it proposes (1) an open-loop inchworm-inspired collaborative motion control strategy that minimizes the accumulated motion errors of the swarm, (2) a closed-loop tether-based collaborative motion control strategy that bounds the motion errors of the swarm, and (3) a constrained motion planning methodology that plans the motion of the swarm for a multi-task mission at hand, while considering the constraints imposed by collaborative motion control strategies. A millimeter-scale robotic platform was also developed within the framework of this research for experimental illustration of the developed strategies. All contributions were validated through extensive simulated and physical experiments.

For completeness, this Thesis also presents a complementary swarm localization methodology and a complementary connectivity restoration strategy that were used in conjunction with the proposed collaborative motion control strategies.

In addressing the challenges associated with the sensing limitations of robotic swarms, this research has expanded the real-world applications where swarms may be used, and the tools available to achieve such applications.