Behavior-based Robot Research Articles

Mitigating the Effects of Jamming on Autonomous Vehicle Convoys with Behavior-Based Robotics

Mitigating the Effects of Jamming on Autonomous Vehicle Convoys with Behavior-Based Robotics

Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics

Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans under different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error.

A deep reinforcement learning-based multi-agent area coverage control for smart agriculture

Precision agriculture (PA) is a collage of strategies and technologies to optimize operations and decisions in farms by using spatial and temporal variabilities in yield, crops, and soil within an agricultural plot. It is a data-driven technique, therefore, selective treatment of crops and soil, and managing variabilities using robots and smart sensors is the next improvement in PA. In this paper, it is modeled as a multi-agent patrolling problem, where robots visit subregions that required immediate attention in the agricultural field. Furthermore, for area coverage / patrolling task in the agricultural plot, a centralized Convolutional Neural Network (CNN) based Dual Deep Q-learning (DDQN) is proposed. A customized reward function is designed, which rewards worth-visiting idle regions, and punishes undesirable actions. A proposed algorithm has been compared with various algorithms including individual Q-learning (IRL), uniform coverage (UC), and Behavior-Based Robotics coverage (BBR) for different scenarios in the agricultural plots.

Review of Electronics-Free Robotics: Toward a Highly Decentralized Control Architecture

In recent years, conventional model-based motion control has become more challenging owing to the continuously increasing complexity of areas in which robots must operate and navigate. A promising approach for solving this issue is by employing interaction-based robotics, which includes behavior-based robotics, morphological computations, and soft robotics that generate control and computation functions based on interactions between the robot body and environment. These control strategies, which incorporate the diverse dynamics of the environment to generate control and computation functions, may alleviate the limitations imposed by the finite physical and computational resources of conventional robots. However, current interaction-based robots can only perform a limited number of actions compared with conventional robots. To increase the diversity of behaviors generated from body–environment interactions, a robotic body design methodology that can generate appropriate behaviors depending on the various situations and environmental stimuli that arise from them is necessitated. Electronics-free robotics is reviewed herein as a paradigm for designing robots with control and computing functions in each part of the body. In electronics-free robotics, instead of using electrical sensors or computers, a control system is constructed based on only mechanical or chemical reactions. Robotic bodies fabricated using this approach do not require bulky electrical wiring or peripheral circuits and can perform control and computational functions by obtaining energy from a central source. Therefore, by distributing these electronics-free controllers throughout the body, we hope to design autonomous and highly decentralized robotic bodies than can generate various behaviors in response to environmental stimuli. This new paradigm of designing and controlling robot bodies can enable realization of completely electronics-free robots as well as expand the range of conventional electronics-based robot designs.

Lightweight Procedural Animation with Believable Physical Interactions

I describe a procedural animation system that uses techniques from behavior-based robot control, combined with a minimalist physical simulation, to produce believable character motions in a dynamic world. Although less realistic than motion capture or full biomechanical simulation, the system produces compelling, responsive character behavior. It is also fast, supports believable physical interactions between characters such as hugging, and makes it easy to author new behaviors.

Intelligence without Representation: A Historical Perspective

This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by the evolutionary development of our own intelligence and that his approach mirrored how our own intelligence functions. Thus, the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact on artificial intelligence and cognitive theory at the time, as well as on modern-day approaches to AI.

Autonomous Mobile Robot based on BehaviourBased Robotic using V-REP Simulator–Pioneer P3-DX Robot

This article describes the design and implementation of behavior-based robotic (BBR) algorithm on a wheeled mobile robot (WMR) Pioneer P3-DX in a maze exploration mission using V-REP simulator. This robot must trace and search for targets placed randomly on a labyrinth. After successfully meeting the objective, robot runs back to home position using the nearest path. Robot navigation system applies BBR algorithm to reach the target using behavior modules which work simultaneously to obtain the desired robot’s trajectory. The most fundamental behavior which is highly affordable to build on the robot system is a wall-following behavior. To make the robot could follow the wall in a safe, smooth and responsive condition, proportional-integral-derivative (PID) controller is applied. PID controller runs by utilizing the reading of sixteen proximity sensors carried on Pioneer P3-DX robot toward the expected wall distance while the robot is exploring the labyrinth. To ensure the designed system works properly, several tests were conducted, including BBR test and PID controller test. BBR test shows that the system can choose the shortest track when returning to home position. The PID controller test produces robot movement with maximum deviation and settling time for about 0.013 m and 30 seconds, respectively.

Философские основания прикладного антропоморфизма в социальной робототехнике

This article is devoted to the philosophical foundations of anthropomorphism in the context of Human-Robot Interaction (HRI), a new interdisciplinary field of research. On the basis of modern scientific works, a positive concept of anthropomorphism as a cognitive mechanism ensuring human adaptation to a complex external environment is formulated. The theoretical principles of applied anthropomorphism (AA) are being developed to identify the conditions for activating anthropomorphic projections in a user during an HRI act. There are two key factors in the structure of AA: appearance and autonomous behaviour. Asymmetry principle is formulated: behavioral realism is more important than highly anthropomorphic appearance. The principle of coherence is formulated: the necessity of synchronisation of the appearance and the robot's behaviour, that is, the level of development of behavioral patterns of the robotic system determines the degree of its anthropomorphism. Various forms of anthropomorphism are distinguished. These differences can be described in terms of involvement in cognitive activity. Anthropomorphism as passive ascription and simple projection receives negative assessment in social robotics, while anthropomorphism, which is deduced from autonomous robots behaviour or initiated by them, is assessed positively. The epistemological foundations of the robotic revolution of the late 80s-early 90s of the XX century are analysed; the methodology of "behavior-based robotics" is examined thoroughly. The behavioral approach in robotics is based on the concept of weak artificial intelligence, within which computational operations and functions of a machine represent concatenation of processes and can lead to the illusion of intelligence in a robot, primarily due to projective intelligence from a human-observer side. These questions are analysed in the context of modern philosophical theories, such as second-order cybernetics, autopoiesis. Anthropomorphism as active ascription of cognitive or emotional states to the robot from the observer side in order to rationalise the behaviour of the object is correlated with D. Dennett's intentional stance. At the end of the article, the phenomenon of attribution of human characteristics to non-human entities in Eastern religious cults is studied. The question of the ontological status of gods and robots is raised.

Performance verification for robot missions in uncertain environments

Establishing a-priori mission performance guarantees is crucial if autonomous robots are to be used with confidence in missions where failure could incur high costs in life and property damage. Automatic mission software verification, in addition to simulation and experimental benchmarking, is a key component of the solution for establishing performance guarantees. This component requires automatically verifying that the software constructed by the mission designer when executed in a partially known environment will adhere to the performance guarantee. In prior work we developed VIPARS, a unique approach to verifying performance guarantees for autonomous behavior-based robot software based on a combination of static analysis and Bayesian networks. While that approach produced fast and accurate verification of single robot missions with robot motion uncertainty, it did not address multiple-robot missions or any form of uncertainty related to environment geometry.This paper addresses the challenges involved in building a software tool for verifying the behavior of a multi-robot waypoint mission that includes uncertainly located obstacles and uncertain environment geometry as well as uncertainty in robot motion. An approach is presented to the problem of a-priori specification of uncertain environments for robot program verification. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach and an approach that allows run-time localization code to be embedded in verification. Verification and experimental validation results are presented for several autonomous robot missions, demonstrating the accuracy of verification and the mission-specific benefit of localization

Genetic Algorithm Seeding of Idiotypic Networks for Mobile-Robot Navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Intuitive control of mobile robots: an architecture for autonomous adaptive dynamic behaviour integration.

In this paper, we present a novel approach to human-robot control. Taking inspiration from behaviour-based robotics and self-organisation principles, we present an interfacing mechanism, with the ability to adapt both towards the user and the robotic morphology. The aim is for a transparent mechanism connecting user and robot, allowing for a seamless integration of control signals and robot behaviours. Instead of the user adapting to the interface and control paradigm, the proposed architecture allows the user to shape the control motifs in their way of preference, moving away from the case where the user has to read and understand an operation manual, or it has to learn to operate a specific device. Starting from a tabula rasa basis, the architecture is able to identify control patterns (behaviours) for the given robotic morphology and successfully merge them with control signals from the user, regardless of the input device used. The structural components of the interface are presented and assessed both individually and as a whole. Inherent properties of the architecture are presented and explained. At the same time, emergent properties are presented and investigated. As a whole, this paradigm of control is found to highlight the potential for a change in the paradigm of robotic control, and a new level in the taxonomy of human in the loop systems.

CBBR: enabling distributed shared memory-based coordination among mobile robots

Coordinating mobile robots are widely used in commercial and industrial settings to fulfill various tasks. However, to program the coordination among mobile robots is challenging. A coordination framework is needed to shield the programmer from handling low-level details of robot control and communication, while supporting flexible and cost-effective coordination at the same time. The coordination framework should also be able to well coexist with the underlying robot control. To this end, we propose the Coordination-enabled Behavior-Based Robotics (CBBR) framework. CBBR employs Distributed Shared Memory (DSM) to support coordination. The shared memory illusion built by the DSM greatly simplifies the coordination logic. Moreover, the flexible access patterns of the DSM and the rich consistency semantics of the DSM reads and writes enable flexible and cost-effective coordination. With the coordination support from the DSM, CBBR naturally extends the classical Behavior-Based Robotics (BBR) for robot control. From the perspective of robot control using BBR, the shared variables in the DSM act as the logical sensors capturing the status of coordination. The coordination algorithms are encapsulated into coordination behaviors. Thus, the physical environment status and the coordination status may trigger the physical and the coordination behaviors. The scheduling of both types of behaviors integrates coordination into robot control. We conduct a case study to demonstrate the use of CBBR. The performance measurements show the cost-effectiveness of coordinating mobile robots based on CBBR, in terms of time, space, and energy consumption.

Towards an aggregate architecture: designed granular systems as programmable matter in architecture

Aggregate architectures are full-scale spatial formations made from loose granular matter. Especially if the individual grain is custom-designed, the range of behaviours can be calibrated to match a wide range of architectural and structural performance criteria. The aggregate becomes programmable matter. The relevance of loose granular systems for architecture is on the one hand their rapid re-configurability, allowing for a system not to be destroyed but rather to be recycled. On the other hand aggregates per se can be functionally graded either within one and the same particle type or through mixing different particle geometries. This enables the variation of architectural properties throughout one and the same material system, which is one of the core postulates of current architectural design research. However, very few examples of designed granular matter in architecture exist. The results presented here are thus one of the first coherent bodies of comprehensive research in this field compiled over a period of five years. Methodologically aggregate systems challenge conventional architectural design principles: whereas an architect generally precisely defines local and global geometry of a structure, in a designed granular system he can only calibrate the particle geometry in order to tune the overall behaviour of the aggregate formation. Thus new design methods have been developed throughout the research projects, which are informed by the related fields of granular physics and behaviour-based robotics. In this context the article provides an introduction to both designed particle systems and suitable fabrication approaches in an architectural context. Case study projects serve to verify the applicability of the concepts introduced. The research findings are discussed with regards to their practical, methodological and design theoretical contributions. To conclude, further directions of research are highlighted.

Performance Verification for Behavior-Based Robot Missions

Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means that the state space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.

SLAM-Based Spatial Memory for Behavior-Based Robots

Knowledge is essential for an autonomous robot to act intelligently when tasked with a mission. With recent leaps of progress, the paradigm of SLAM (Simultaneous Localization and Mapping) has emerged as an ideal source of spatial knowledge for autonomous robots. However, despite advancements in both paradigms of SLAM and robot control, research in the integration of these areas has been lacking and remained open to investigation. This paper presents an integration of SLAM into a behavior-based robotic system as a dynamically acquired spatial memory, which can be used to enable new behaviors and augment existing ones. The effectiveness of the integrated system is demonstrated with a biohazard search mission, where a robot is tasked to search and locate a biohazard within an unknown environment under a time constraint.

A New Navigation of Behavior-Based Olfactory Mobile Robot

In this paper a new olfactory mobile robot application is proposed where dynamic olfaction system is used on a mobile robot in order to acquire the gas/odour property of objects. Olfaction system with two dynamic gas/odour sensors can be moved in 14180o' type="#_x0000_t75"> in order to be able to detect source in many directions. We examine the problem of deciding when, how and where the gas/odour sensor should be activated by planning for active perception use behavior-based architecture. Simple form of cooperation between Fuzzy Logic control and Particle Swarm Optimization (PSO) is implemented in the navigation strategies. The real experiments performed on a simple mobile robot equipped with dynamic gas/odour sensor and three infra-red sensor. The initial result shows that olfactory mobile robot that is capable of locating the source of a simulated chemical leak in an environment while detecting and avoiding obstacles along its path.

A new procedure for multi-mode sequential flocking with application to multiple non-holonomic mobile robot motion control: mode description and integration principle

This is the first of two-part paper that investigates the multi-mode sequential flocking with application to multiple non-holonomic mobile robot motion control. To provide a new procedure to avoid collisions and obstacles in the process of multi-robot’s sequential flocking, this paper presents the design of a multi-model flocking control for multiple mobile robots in terms of behaviour-based robotics. Some nature-imitating behaviour modes are integrated into the new sequential flocking strategy, including single-robot potential-based behaviour, single-robot wall-following behaviour, multi-robot rigid-body bouncing behaviour, multi-robot path-tracking behaviour and their fusion state. In this way, the efficient collision and obstacle avoidance in flocking motion can be achieved.

Dynamic Game Difficulty Scaling Using Adaptive Behavior-Based AI

Games are played by a wide variety of audiences. Different individuals will play with different gaming styles and employ different strategic approaches. This often involves interacting with nonplayer characters that are controlled by the game AI. From a developer's standpoint, it is important to design a game AI that is able to satisfy the variety of players that will interact with the game. Thus, an adaptive game AI that can scale the difficulty of the game according to the proficiency of the player has greater potential to customize a personalized and entertaining game experience compared to a static game AI. In particular, dynamic game difficulty scaling refers to the use of an adaptive game AI that performs game adaptations in real time during the game session. This paper presents two adaptive algorithms that use ideas from reinforcement learning and evolutionary computation to improve player satisfaction by scaling the difficulty of the game AI while the game is being played. The effects of varying the learning and mutation rates are examined and a general rule of thumb for the parameters is proposed. The proposed algorithms are demonstrated to be capable of matching its opponents in terms of mean scores and winning percentages. Both algorithms are able to generalize well to a variety of opponents.

Real-time behavior-based robot control

Behavior-based systems form the basis of autonomous control for many robots, but there is a need to ensure these systems respond in a timely manner. Unexpected latency can adversely affect the quality of an autonomous system's operations, which in turn can affect lives and property in the real-world. A robots ability to detect and handle external events is paramount to providing safe and dependable operation. This paper presents a concurrent version of a behavior-based system called the Real-Time Unified Behavior Framework, which establishes a responsive basis of behavior-based control that does not bind the system developer to any single behavior hierarchy. The concurrent design of the framework is based on modern software engineering principles and only specifies a functional interface for components, leaving the implementation details to the developers. In addition, the individual behaviors are executed by a real-time scheduler, guaranteeing the responsiveness of routines that are critical to the autonomous system's safe operation. Experimental results demonstrate the ability of this approach to provide predictable temporal operation, independent of fluctuations in high-level computational loads.

Using Sun’s Java Real-Time System to Manage Behavior-Based Mobile Robot Controllers

Implementing a robot controller that can effectively manage limited resources in a deterministic, real-time manner is challenging. Behavior-based architectures that decompose autonomy into levels of intelligence are popular due to their robustness but do not provide real-time features that enforce timing constraints or support determinism. We propose an architecture and approach for using the real-time features of the Real-Time Specification for Java (RTSJ) in a behavior-based mobile robot controller to show that timing constraints affect performance. This is accomplished by extending a real-time aware architecture that explicitly enumerates timing requirements for each behavior. It is not enough to reduce latency. The usefulness of this approach is demonstrated via an implementation on Solaris 10 and the Sun Java Real-Time System (Java RTS). Experimental results are obtained using a K-team Koala robot performing path following with four composite behaviors. Experiments were conducted using several task period sets in three cases: real-time threads with the real-time garbage collector, real-time threads with the non- real-time garbage collector, and non-real-time threads with the non-real-time garbage collector. Results show that even if latency and determinism are improved, the timing of each individual behavior significantly affects task performance.