Deployment Of Mobile Robots Research Articles

A radioactive source-seeking method based on angle constraint and particle diffusion

The deployment of mobile robots to conduct search for radioactive sources plays a crucial role in preventing radiation pollution and safeguarding public health. And it is a significant challenge to accurately locate radioactive sources in unknown environments. Herein, a particle filter based on mobile robot search method is proposed to localize radiation sources. By applying an angle constraint technique, the complexity of this algorithm is reduced, the high accuracy of source estimation is maintained, and the range of search is expanded. Additionally, a particle diffusion technology is applied to address particle loss that occurs during filtering. Furthermore, an adaptive gradient descent strategy is adopted to enhance the search efficiency. Experimental results demonstrate that this method achieves a success rate of over 95 % within a rectangular area (e.g., 40m by 40m), outperforming traditional methods, and it could perform effectively in dual radioactive source search tasks.

Resilient Ground Vehicle Autonomous Navigation in GPS-Denied Environments

Co-design and integration of vehicle navigation and control and state estimation is key for enabling field deployment of mobile robots in GPS-denied cluttered environments, and sensor calibration is critical for successful operation of both subsystems. This paper demonstrates the potential of this co-design approach with field tests of the integration of a reactive receding horizon-based motion planner and controller with an inertial aided multi-sensor calibration scheme. The reported method provides accurate calibration parameters that improve the performance of the state estimator, and enable the motion controller to generate smooth and continuous minimal-jerk trajectories based on local LiDAR data. Numerical simulations in Unity, and real-world experimental results from the field corroborate the claims of efficacy for the reported autonomous navigation computational pipeline.

Semi‐uniform deployment of mobile robots in perfect ℓ$$ \ell $$‐ary trees

SummaryIn this paper, we consider the problem of semi‐uniform deployment for mobile robots in perfect ‐ary trees. This problem requires robots to spread in the tree so that, for some positive integer and some fixed integer , each node of depth is occupied by a robot. Robots have an infinite visibility range but are opaque, and each robot can emit a light color visible to itself and other robots, taken from a set of colors, at each time step. Then, we clarify the solvability of the semi‐uniform deployment problem, focusing on the number of available light colors. First, we consider robots with . In this setting, we show that there is no collision‐free algorithm to solve the problem. Next, relax the number of available light colors, that is, we consider robots with . In this setting, we propose a collision‐free algorithm that can solve the problem. From these results, we can show that the semi‐uniform deployment problem can be solved when , and our proposed algorithm is optimal with respect to the number of used light colors (i.e., 2).

APPL: Adaptive Planner Parameter Learning

While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manual parameter re-tuning by human robotics experts in order to function in new environments. Furthermore, even for just one complex environment, a single set of fine-tuned parameters may not work well in different regions of that environment. These problems prohibit reliable mobile robot deployment by non-expert users. As a remedy, we propose Adaptive Planner Parameter Learning (appl), a machine learning framework that can leverage non-expert human interaction via several modalities – including teleoperated demonstrations, corrective interventions, and evaluative feedback – and also unsupervised reinforcement learning to learn a parameter policy that can dynamically adjust the parameters of classical navigation systems in response to changes in the environment. appl inherits safety and explainability from classical navigation systems while also enjoying the benefits of machine learning, i.e., the ability to adapt and improve from experience. We present a suite of individual appl methods and also a unifying cycle-of-learning scheme that combines all the proposed methods in a framework that can improve navigation performance through continual, iterative human interaction and simulation training.

Explicit feature disentanglement for visual place recognition across appearance changes

In the long-term deployment of mobile robots, changing appearance brings challenges for localization. When a robot travels to the same place or restarts from an existing map, global localization is needed, where place recognition provides coarse position information. For visual sensors, changing appearances such as the transition from day to night and seasonal variation can reduce the performance of a visual place recognition system. To address this problem, we propose to learn domain-unrelated features across extreme changing appearance, where a domain denotes a specific appearance condition, such as a season or a kind of weather. We use an adversarial network with two discriminators to disentangle domain-related features and domain-unrelated features from images, and the domain-unrelated features are used as descriptors in place recognition. Provided images from different domains, our network is trained in a self-supervised manner which does not require correspondences between these domains. Besides, our feature extractors are shared among all domains, making it possible to contain more appearance without increasing model complexity. Qualitative and quantitative results on two toy cases are presented to show that our network can disentangle domain-related and domain-unrelated features from given data. Experiments on three public datasets and one proposed dataset for visual place recognition are conducted to illustrate the performance of our method compared with several typical algorithms. Besides, an ablation study is designed to validate the effectiveness of the introduced discriminators in our network. Additionally, we use a four-domain dataset to verify that the network can extend to multiple domains with one model while achieving similar performance.

Combining Probabilistic Planning and Logic Programming on Mobile Robots

Key challenges to widespread deployment of mobile robots to interact with humans in real-world domains include the ability to: (a) robustly represent and revise domain knowledge; (b) autonomously adapt sensing and processing to the task at hand; and (c) learn from unreliable high-level human feedback. Partially observable Markov decision processes (POMDPs) have been used to plan sensing and navigation in different application domains. It is however a challenge to include common sense knowledge obtained from sensory or human inputs in POMDPs. In addition, information extracted from sensory and human inputs may have varying levels of relevance to current and future tasks. On the other hand, although a non-monotonic logic programming paradigm such as Answer Set Programming (ASP) is wellsuited for common sense reasoning, it is unable to model the uncertainty in real-world sensing and navigation (Gelfond 2008). This paper presents a hybrid framework that integrates ASP, hierarchical POMDPs (Zhang and Sridharan 2012) and psychophysics principles to address the challenges stated above. Experimental results in simulation and on mobile robots deployed in indoor domains show that the framework results in reliable and efficient operation.

Engaging End Users in Designing Systems and Hardware for a Socially Assistive Robot

Development of low-cost robots to assist older adults requires the input of end users: older adults, paid caregivers and clinicians. This study builds on prior work focused on the task investigation and deployment of mobile robots in a Program of All-inclusive Care for the Elderly. We identified hydration, walking and reaching as tasks appropriate for the robot and helpful to the older adults. In this study we investigated the design specifications for a socially assistive robot to perform the above tasks. Through focus groups of clinicians, older adults and paid caregivers we sought preferences on the design specifications. Using conventional content analysis, the following four themes emerged: the robot must be polite and personable; science fiction or alien like; depends on the need of the older adult; and multifaceted to meet the needs of older adults. These themes were used in the design and deployment of the Quori robot.

User preference-aware navigation for mobile robot in domestic via defined virtual area

Human-aware robot navigation is the key to facilitate the deployment of mobile robots into human–robot shared environments. Although many efforts have been made in this regard, almost all of the existing works focus on the constraints such as user comfort and social rules, while ignoring to consider user preferences. And it is expected that the mobile robot is able to navigate in the user-desired areas according to user preferences, e.g., navigating at low speed in the bedroom. To this end, this paper studies the problem of allowing users to control the robot’s working areas to ensure robot aware navigation based on their preferences. To address the intrusiveness of additional facilities or devices to users, we propose a non-intrusive solution by deploying the virtual areas that are non-physical areas but are respected by the mobile robot while performing navigation tasks. Farther, an interactive interface is developed to support the common (non-expert) users to flexibly define the desired areas and specify the navigation behavior of the robot according to their preferences. The proposed solution is fully evaluated through extensive experiments. Experimental results are presented to demonstrate the validity of our approach, and show that the mobile robot can change its navigation behavior in user-defined areas according to user preferences. Supplementary video can be available at https://youtu.be/oXtbNavLmMk.

Packet forwarding mechanism for mobile robots bounded by random walk model

With the evolution of technology search and rescue operations are now been executed with the help of mobile robots. Wired mobile robot deployment is very cumbersome and constrained. Thus, the need arises for appropriate wireless technology that may support the same or even better functionality. ZigBee is gaining popularity as supporting technology to facilitate the working of mobile robots for search and rescue operations. This is because ZigBee based nodes utilize the industrial, scientific, and medical frequency band that supports a data rate up to 250 kbps. This paper presents a packet forwarding mechanism for ZigBee based mobile wireless sensor network nodes. The results achieved through the reported work shows that the packet loss rate comes around to be 2 percent resulting in a 98 percent successful packet reception at the receiver end.

Two Mathematical Programming-Based Approaches for Wireless Mobile Robot Deployment in Disaster Environments

This paper addresses the issue of the wireless mobile robot deployment for the ad hoc network establishment in disaster environments, which aims to maximize the important locations covered by the established ad hoc network so as to improve the performance of task allocation. In many disaster environments, the number of wireless mobile robots usually is much less than the number of important locations in the environment so that maximizing the important locations covered by the established ad hoc network is the primary objective of wireless mobile robot deployment approaches. To maximize the coverage of important locations, most of the current approaches were developed based on greedy algorithms. Due to the myopia of greedy algorithms, these approaches can only maximize the coverage of important locations of each wireless mobile robot rather than the whole network. To this end, two mathematical programming-based wireless mobile robot deployment approaches are proposed for ad hoc network establishment in disaster environments. The proposed approach can create suitable deployment locations for all wireless mobile robots in a disaster environment. The experimental results demonstrate that ad hoc networks established by the proposed approaches can cover more important locations in a disaster environment than those established by greedy algorithm-based approaches.

An Innovative Approach for Ad Hoc Network Establishment in Disaster Environments by the Deployment of Wireless Mobile Agents

In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots, searching and establishing ad hoc networks in disaster environments is a challenging issue in both theory and practice. To this end, a task-based wireless mobile robot deployment approach is proposed in this article. The proposed approach consists of a search process and a deployment process. The search process can guide wireless mobile robots to efficiently find tasks in unknown and complex environments. The deployment process can find suitable deployment locations for wireless mobile robots to establish ad hoc networks. The established ad hoc networks can ensure the communication of wireless mobile robots in the network and can cover the maximum number of task locations and the maximum areas in a disaster environment. Experimental results demonstrate that based on the proposed approach, wireless mobile robots have better performance in terms of search and ad hoc network establishment in disaster environments.

Rapid Deployment of Mobile Robots Under Temporal, Performance, Perception, and Resource Constraints

We consider the problem where a team of mobile robots is tasked with collecting information about a set of stationary targets. There is a temporal deadline to complete the task, and the objective is to determine a control policy maximizing the probability of successfully completing the task within the assigned deadline. In addition, robots use imprecise sensors, and are subject to noisy dynamics. Furthermore, there are more targets than robots, so load sharing between robots is necessary. We model this problem using the theory of constrained Markov decision processes and split the solution into two steps. First, policies to observe small subsets of targets are computed, and the proposed model and algorithm allow one to extract accurate information characterizing the performance of the computed control policies. In the second stage, a subset of the computed policies is assigned to the robots for execution with the objective of maximizing a collective team performance function. To this end, we introduce a submodular objective function and a greedy approximation algorithm to solve this nonlinear assignment problem. Simulations demonstrate how these models can be used in practice to appropriately tune the parameters characterizing this problem and show how the approach favorably scales with the complexity of the problem.

Spatio-temporal exploration strategies for long-term autonomy of mobile robots

We present a study of spatio-temporal environment representations and exploration strategies for long-term deployment of mobile robots in real-world, dynamic environments. We propose a new concept for life-long mobile robot spatio-temporal exploration that aims at building, updating and maintaining the environment model during the long-term deployment. The addition of the temporal dimension to the explored space makes the exploration task a never-ending data-gathering process, which we address by application of information-theoretic exploration techniques to world representations that model the uncertainty of environment states as probabilistic functions of time. We evaluate the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months. The combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of continuously changing environments, enabling efficient and self-improving long-term operation of mobile robots.

Active Visual Planning for Mobile Robot Teams Using Hierarchical POMDPs

Key challenges to widespread deployment of mobile robots include collaboration and the ability to tailor sensing and information processing to the task at hand. Partially observable Markov decision processes (POMDPs), which are an instance of probabilistic sequential decision-making, can be used to address these challenges in domains characterized by partial observability and nondeterministic action outcomes. However, such formulations tend to be computationally intractable for domains that have large complex state spaces and require robots to respond to dynamic changes. This paper presents a hierarchical decomposition of POMDPs that incorporates adaptive observation functions, constrained convolutional policies, and automatic belief propagation, enabling robots to retain capabilities for different tasks, direct sensing to relevant locations, and determine the sequence of sensing and processing algorithms best suited to any given task. A communication layer is added to the POMDP hierarchy for belief sharing and collaboration in a team of robots. All algorithms are evaluated in simulation and on physical robots, localizing target objects in dynamic indoor domains.

Self-organized multi-camera network for ubiquitous robot deployment in unknown environments

In this paper, we present a multi-agent system based on a network of intelligent cameras for the easy and fast deployment of mobile robots in unknown environments. The cameras are able to detect events which require the presence of the robots, calculate routes of cameras through which the robots can navigate, and support this navigation. A route is a list of cameras connected by neighbourhood relationships: the cameras may be neighbours if their Fields of View (FOVs) overlap, or if there exists a passable path among them (if their FOVs do not overlap). In our system, all coordination processes are fully distributed, based only on local-interactions, and self-organization. Our system is robust and redundant, and scales well with the size of the environment and the number of cameras and robots. Finally, it is flexible to the environment, to the number of agents used, and to their disposition. In the experimental section, we show the performance of this system in different real world settings.

Color learning and illumination invariance on mobile robots: A survey

Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the inter-dependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.

Sociable Robots Through Self-Maintained Energy

Research of autonomous mobile robots has mostly emphasized interaction and coordination that are natually inspired from biological behavior of birds, insects, and fish: flocking, foraging, collecting, and sharing. However, most research has been only focused on autonomous behaviors in order to perform robots like animals, whereas it is lacked of determinant to those behaviours: energy. Approaching to clusted amimal and the higher, collective and sharing food among individuals are major activity to keep society being. This paper issues an approach to sociable robots using self-maintained energy in cooperative mobile robots, which is dominantly inspired from swarm behavior of collecting and sharing food of honey-bee and ant. Autonomous mobile robots are usually equipped with a finite energy, thus they can operate in a finite time. To overcome the finitude, we describe practical deployment of mobile robots that are capable of carrying and exchanging fuel to other robots. Mechanism implementation including modular hardware and control architecture to demonstrate the capabicities of the approach is presented. Subsequently, the battery exchange algorithm basically based on probabilistic modeling of total energy on each robot located in its local vicinity is described. The paper is concluded with challenging works of chain of mobile robots, rescue, repair, and relation of heterogeneous robots.

Deployment of mobile robots with energy and timing constraints

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.