7 research outputs found
Design and Motion Planning for a Reconfigurable Robotic Base
A robotic platform for mobile manipulation needs to satisfy two contradicting
requirements for many real-world applications: A compact base is required to
navigate through cluttered indoor environments, while the support needs to be
large enough to prevent tumbling or tip over, especially during fast
manipulation operations with heavy payloads or forceful interaction with the
environment. This paper proposes a novel robot design that fulfills both
requirements through a versatile footprint. It can reconfigure its footprint to
a narrow configuration when navigating through tight spaces and to a wide
stance when manipulating heavy objects. Furthermore, its triangular
configuration allows for high-precision tasks on uneven ground by preventing
support switches. A model predictive control strategy is presented that unifies
planning and control for simultaneous navigation, reconfiguration, and
manipulation. It converts task-space goals into whole-body motion plans for the
new robot. The proposed design has been tested extensively with a hardware
prototype. The footprint reconfiguration allows to almost completely remove
manipulation-induced vibrations. The control strategy proves effective in both
lab experiment and during a real-world construction task.Comment: 8 pages, accepted for RA-L and IROS 202
Task Allocation in Foraging Robot Swarms:The Role of Information Sharing
Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms