Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing

In this paper we propose a novel algorithm, Wi-Closure, to improve computational efficiency and robustness of loop closure detection in multi-robot SLAM. Our approach decreases the computational overhead of classical approaches by pruning the search space of potential loop closures, prior to evaluation by a typical multi-robot SLAM pipeline. Wi-Closure achieves this by identifying candidates that are spatially close to each other by using sensing over the wireless communication signal between robots, even when they are operating in non-line-of-sight or in remote areas of the environment from one another. We demonstrate the validity of our approach in simulation and hardware experiments. Our results show that using Wi-closure greatly reduces computation time, by 54% in simulation and by 77% in hardware compared, with a multi-robot SLAM baseline. Importantly, this is achieved without sacrificing accuracy. Using Wi-Closure reduces absolute trajectory estimation error by 99% in simulation and 89.2% in hardware experiments. This improvement is due in part to Wi-Closure's ability to avoid catastrophic optimization failure that typically occurs with classical approaches in challenging repetitive environments.Comment: 6 pages without reference

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots

As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners. Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree. For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster

Communication Aware Mobile Robot Teams

The type of scenarios that could benefit from a team of robots that are able to self configure into an ad-hoc multi-hop mobile communication network while completing a task in an unknown environment, range from search and rescue in a partially collapsed building to providing a security perimeter around a region of interest. In this thesis, we present a hybrid system that enables a team of robots to maintain a prescribed end-to-end data rate while moving through a complex unknown environment, in a distributed manner, to complete a specific task. This is achieved by a systematic decomposition of the real-time situational awareness problem into subproblems that can be efficiently solved by distributed optimization. The validity of this approach is demonstrated through multiple simulations and experiments in which the a team of robots is able to accurately map an unknown environment and then transition to complete a traditional situational awareness task. We also present MCTP, a lightweight communication protocol that is specifically designed for use in ad-hoc multi-hop wireless networks composed of low-cost low-power transceivers. This protocol leverages the spatial diversity found in mobile robot teams as well as recently developed robust routing systems designed to minimize the variance of the end-to-end communication link. The combination of the hybrid system and MCTP results in a system that is able to complete a task, with minimal global coordination, while providing near loss-less communication over an ad-hoc multi-hop network created by the members of the team in unknown environments

Asymptotically Optimal Sampling-Based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews, https://www.annualreviews.org/. 25 pages. 2 figure

Problems in Control, Estimation, and Learning in Complex Robotic Systems

In this dissertation, we consider a range of different problems in systems, control, and learning theory and practice. In Part I, we look at problems in control of complex networks. In Chapter 1, we consider the performance analysis of a class of linear noisy dynamical systems. In Chapter 2, we look at the optimal design problems for these networks. In Chapter 3, we consider dynamical networks where interactions between the networks occur randomly in time. And in the last chapter of this part, in Chapter 4, we look at dynamical networks wherein coupling between the subsystems (or agents) changes nonlinearly based on the difference between the state of the subsystems. In Part II, we consider estimation problems wherein we deal with a large body of variables (i.e., at large scale). This part starts with Chapter 5, in which we consider the problem of sampling from a dynamical network in space and time for initial state recovery. In Chapter 6, we consider a similar problem with the difference that the observations instead of point samples become continuous observations that happen in Lebesgue measurable observations. In Chapter 7, we consider an estimation problem in which the location of a robot during the navigation is estimated using the information of a large number of surrounding features and we would like to select the most informative features using an efficient algorithm. In Part III, we look at active perception problems, which are approached using reinforcement learning techniques. This part starts with Chapter 8, in which we tackle the problem of multi-agent reinforcement learning where the agents communicate and classify as a team. In Chapter 9, we consider a single agent version of the same problem, wherein a layered architecture replaces the architectures of the previous chapter. Then, we use reinforcement learning to design the meta-layer (to select goals), action-layer (to select local actions), and perception-layer (to conduct classification)

Distributed, Scalable And Resilient Information Acquisition For Multi-Robot Teams

Advances in robotic mobility and sensing technology have the potential to provide newcapabilities in a wide variety of information acquisition problems including environmental monitoring, structure inspection, localization and mapping of unknown environments, and search and rescue, amongst many others. In particular, teams composed of multiple robots have shown great potential in solving these problems, though it is challenging to design efficient algorithms that are distributed and scale well, and even more complex in hazardous or challenging environments. The purpose of this dissertation is to provide novel algorithms to the capabilities of multi-robot teams to gather information which are distributed, scalable, and resilient. The first part of the dissertation introduces the single-robot information acquisition problem, and focuses on algorithms that may be used for individual robots to plan their own trajectories. The methods presented here are search-based, meaning that an individual robot has a finite set of actions and is seeking to efficiently build a search tree over a known planning horizon. The first method presented details how to use the concept of algebraic redundancy and closeness to achieve a smooth trade-off of completeness in the exploration process, as an anytime planning algorithm. Next we show how a single robot can compute an admissible and consistent heuristic which guides the search towards the most informative regions of the state space, using the classic A* planning algorithm, drastically improving the search efficiency. The next chapter of the dissertation focuses on how to build on the single robot planning algorithms to create efficient algorithms for multi-robot teams, which operate in a distributed manner and scalable manner. The first method presented is coordinate descent, 5 otherwise known in the literature as sequential greedy assignment. This algorithm is implemented in a multi-robot target tracking hardware experiment. Next, we formulate an energy-aware multi-robot information acquisition problem, which allows for heterogeneity and captures trade-offs between information and energy expenditure. However, this results in a non-monotone objective function. Therefore we propose a new algorithm based on distributed local search, which achieves performance guarantees through a diminishing returns property known as submodularity. The final chapter focuses on hazardous or failure prone environments that necessitate resilience to a fixed number of failures in the multi-robot team. We provide a definition of resilience, and formulate a resilient information acquisition problem. We then propose the first algorithm that solves this problem through an online application of robust trajectory planning, and provide theoretical guarantees on its performance. We then present three unique applications of the resilient multi-robot information acquisition framework, including target tracking, occupancy grid mapping, and persistent surveillance which demonstrate the efficacy of our approach

Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo

The processes of information diffusion across social networks (for example, the spread of opinions and the formation of beliefs) are attracting substantial interest in disciplines ranging from behavioral sciences to mathematics and engineering (see "Summary"). Since the opinions and behaviors of each individual are infl uenced by interactions with others, understanding the structure of interpersonal infl uences is a key ingredient to predict, analyze, and, possibly, control information and decisions [1]. With the rapid proliferation of social media platforms that provide instant messaging, blogging, and other networking services (see "Online Social Networks") people can easily share news, opinions, and preferences. Information can reach a broad audience much faster than before, and opinion mining and sentiment analysis are becoming key challenges in modern society [2]. The first anecdotal evidence of this fact is probably the use that the Obama campaign made of social networks during the 2008 U.S. presidential election [3]. More recently, several news outlets stated that Facebook users played a major role in spreading fake news that might have infl uenced the outcome of the 2016 U.S. presidential election [4]. This can be explained by the phenomena of homophily and biased assimilation [5]-[7] in social networks, which correspond to the tendency of people to follow the behaviors of their friends and establish relationships with like-minded individuals

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available