Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

/pdf/critical-design-and-control-issues-of-indoor-autonomous-2em5147ys5.pdf

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supp...

Advances in Inference and Representation for Simultaneous Localization and Mapping

Multi-Agent Path Finding (MAPF) is the problem of planning collision-free paths for multiple agents in a shared environment. In this paper, we propose a novel algorithm MAPF-LNS2 based on large neighborhood search for solving MAPF efficiently. Starting from a set of paths that contain collisions, MAPF-LNS2 repeatedly selects a subset of colliding agents and replans their paths to reduce the number of collisions until the paths become collision-free. We compare MAPF-LNS2 against a variety of state-of-the-art MAPF algorithms, including Prioritized Planning with random restarts, EECBS, and PPS, and show that MAPF-LNS2 runs significantly faster than them while still providing near-optimal solutions in most cases. MAPF-LNS2 solves 80% of the random-scenario instances with the largest number of agents from the MAPF benchmark suite with a runtime limit of just 5 minutes, which, to our knowledge, has not been achieved by any existing algorithms.

/pdf/mapf-lns2-fast-repairing-for-multi-agent-path-finding-via-298iq72p.pdf

MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search

The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system objective, which may be realized by a reactive, or planning, control algorithm. We also present several approaches aimed at minimizing flocking communication and computational requirements in real systems via neighbor filtering and event-driven planning. We conclude the overview with our perspective on the outlook and future research direction of optimal flocking algorithms.

An Overview on Optimal Flocking

Living systems at all scales aggregate in large numbers for a variety of functions including mating, predation, and survival The majority of such systems consist of unconnected individuals that collectively flock, school, or swarm However, some aggregations involve physically entangled individuals, which can confer emergent mechanofunctional material properties to the collective Here, we study in laboratory experiments and rationalize in theoretical and robophysical models the dynamics of physically entangled and motile self-assemblies of 1-cm-long California blackworms (Lumbriculus variegatus, Annelida: Clitellata: Lumbriculidae) Thousands of individual worms form braids with their long, slender, and flexible bodies to make a three-dimensional, soft, and shape-shifting "blob" The blob behaves as a living material capable of mitigating damage and assault from environmental stresses through dynamic shape transformations, including minimizing surface area for survival against desiccation and enabling transport (negative thermotaxis) from hazardous environments (like heat) We specifically focus on the locomotion of the blob to understand how an amorphous entangled ball of worms can break symmetry to move across a substrate We hypothesize that the collective blob displays rudimentary differentiation of function across itself, which when combined with entanglement dynamics facilitates directed persistent blob locomotion To test this, we develop a robophysical model of the worm blobs, which displays emergent locomotion in the collective without sophisticated control or programming of any individual robot The emergent dynamics of the living functional blob and robophysical model can inform the design of additional classes of adaptive mechanofunctional living materials and emergent robotics

Collective dynamics in entangled worm and robot blobs

The task of shape formation in robot swarms can often be reduced to two tasks—assigning goal locations to each robot and creating a collision-free path to that goal. In this article, we present a distributed algorithm that solves these tasks concurrently, enabling a swarm of robots to move and form a shape quickly and without collision. A user can specify a desired shape as an image, send that to a swarm of identically programmed robots, and the swarm will move all robots to goal locations within the desired shape. This algorithm was executed on a swarm of up to 1024 simulated robots and a swarm of 100 real robots, showing that it reliably converges to all robots forming the shape.

Shape Formation in Homogeneous Swarms Using Local Task Swapping

In this paper, we propose an abstraction that captures high-level formation and location-based swarm behaviors, and an automated control synthesis framework to generate correct-by-construction behaviors. Our abstraction includes symbols representing both possible formations and physical locations in the workspace. We allow users to write linear temporal logic (LTL) specifications over the symbols to specify high-level tasks for the swarm. To satisfy a specification, we automatically synthesize a centralized symbolic plan, and environment and swarm-size-dependent motion controllers that are guaranteed to implement the symbolic transitions. In addition, using integer programming (IP), we assign robots to different sub-swarms to execute the synthesized symbolic plan. Our framework gives insights into controlling a large fleet of autonomous robots to achieve complex tasks which require composition of behaviors at different locations and coordination among different groups of robots in a correct-by-construction way. We demonstrate the proposed framework in simulation with 16 UAVs and 8 ground vehicles, and on a physical platform with 20 ground robots, showcasing the generality of the approach and discussing the implications of controlling constrained physical hardware.

Automatic Control Synthesis for Swarm Robots from Formation and Location-based High-level Specifications

This paper describes a distributed algorithm for computing the number of robots in a swarm, only requiring communication with neighboring robots. The algorithm can adjust the estimated count when the number of robots in the swarm changes, such as the addition or removal of robots. Probabilistic guarantees are given, which show the accuracy of this method, and the trade-off between accuracy, speed, and adaptability to changing numbers. The proposed approach is demonstrated in simulation as well as a real swarm of robots.

A Fast, Accurate, and Scalable Probabilistic Sample-Based Approach for Counting Swarm Size

Complexity, cost, and power requirements for actuation of individual robots are large factors in limiting the size of robotic swarms. Here we present a prototype robotic system that allows for externally powered motion in 2D without sacrificing individual autonomy, which simplifies the robot hardware, possibly enabling larger swarm sizes. This is accomplished using a table surface that is moving in an orbital fashion, and where robots can move to any point on the table surface simply through a series of carefully timed attachment and detachment steps. We present a model for the robot's motion, and use this model to create a motion controller that allows the robot to move from its current position to any other position on the table in approximately a straight line. We show this controller working in simulation as well as on an experimental hardware system.

Autonomous mobile robot with independent control and externally driven actuation

This letter presents a decentralized algorithm that allows a swarm of identically programmed agents to cooperatively estimate their global poses using local range and bearing measurements. The design of our algorithm explicitly considers the phase asynchrony of each agent's local clock, moreover, the execution of our algorithm does not require each agent to actively keep the same neighbors over time. A theoretical analysis about the effect of each agent's sensing noise and communication loss is given, in addition, we validate the presented algorithm via experiments running on a swarm of up to 256 simulated robots and a swarm of 100 physical robots. The results from the experiments show that the presented algorithm allows each agent to estimate its global pose quickly and reliably. Video of 100 robots executing the presented algorithm as well as supplementary material can be found in  [1] and  [2] .

Decentralized Localization in Homogeneous Swarms Considering Real-World Non-Idealities

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