We investigate the problem of moving a mixture of active and passive elements to a desired location using a swarm of robots that require only two bits of sensory input. We examine memory-less control strategies that map a robot's sensory input to the respective wheel velocities.
Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image (or images) from sequential datasets that include both spatial and temporal components. The results suggest that ESNs can capture the temporal structure inherent in VPR problems and improve generalisation abilities, robustness, and accuracy.
This work demonstrates active subtraction as a viable method of self- reconfiguration, without the need for heuristics or stochasticity, and suggests its potential for application in real-world systems.
The field of swarm robotics studies bio-inspired cooperative control strategies for large groups of relatively simple robots. The robots are limited in their individual capabilities, however, by inducing cooperation amongst them, the limitations can be overcome. Local sensing and interactions within the robotic swarm promote scalable, robust, and flexible behaviours. This thesis focuses on synthesising and analysing minimalist control strategies for swarm robotic systems. Using a computation-free swarming framework, multiple decentralised control strategies are synthesised and analysed. The control strategies enable the robots—equipped with only discrete-valued sensors—to reactively respond to their environment.
This paper proposes decentralized and fully reactive controllers for pose control of 3D modular reconfigurable robots. The robots operate in liquid environments, and move by routing fluid through themselves. We prove that robots of convex shape are guaranteed to reach a goal object with a preferred orientation.
We propose stochastic control policies for gathering a group of embodied agents in a two-dimensional square tile environment. We prove that a group of agents, irrespective of initial positions, will almost surely reach a Pareto optimal configuration in finite time.
We study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage.
We examine the problem solving capabilities of swarms of computation- and memory-free agents. Our findings show that the shepherding problem does not fundamentally require arithmetic computation or memory to be solved.