41 research outputs found
Securing multi-robot systems with inter-robot observations and accusations
In various industries, such as manufacturing, logistics, agriculture, defense, search and rescue, and transportation, Multi-robot systems (MRSs) are increasingly gaining popularity. These systems involve multiple robots working together towards a shared objective, either autonomously or under human supervision. However, as MRSs operate in uncertain or even adversarial environments, and the sensors and actuators of each robot may be error-prone, they are susceptible to faults and security threats unique to MRSs. Classical techniques from distributed systems cannot detect or mitigate these threats. In this dissertation, novel techniques are proposed to enhance the security and fault-tolerance of MRSs through inter-robot observations and accusations.
A fundamental security property is proposed for MRSs, which ensures that forbidden deviations from a desired multi-robot motion plan by the system supervisor are detected. Relying solely on self-reported motion information from the robots for monitoring deviations can leave the system vulnerable to attacks from a single compromised robot. The concept of co-observations is introduced, which are additional data reported to the supervisor to supplement the self-reported motion information. Co-observation-based detection is formalized as a method of identifying deviations from the expected motion plan based on discrepancies in the sequence of co-observations reported. An optimal deviation-detecting motion planning problem is formulated that achieves all the original application objectives while ensuring that all forbidden plan-deviation attacks trigger co-observation-based detection by the supervisor. A secure motion planner based on constraint solving is proposed as a proof-of-concept to implement the deviation-detecting security property.
The security and resilience of MRSs against plan deviation attacks are further improved by limiting the information available to attackers. An efficient algorithm is proposed that verifies the inability of an attacker to stealthily perform forbidden plan deviation attacks with a given motion plan and announcement scheme. Such announcement schemes are referred to as horizon-limiting. An optimal horizon-limiting planning problem is formulated that maximizes planning lookahead while maintaining the announcement scheme as horizon-limiting. Co-observations and horizon-limiting announcements are shown to be efficient and scalable in protecting MRSs, including systems with hundreds of robots, as evidenced by a case study in a warehouse setting.
Lastly, the Decentralized Blocklist Protocol (DBP), a method for designing Byzantine-resilient decentralized MRSs, is introduced. DBP is based on inter-robot accusations and allows cooperative robots to identify misbehavior through co-observations and share this information through the network. The method is adaptive to the number of faulty robots and is widely applicable to various decentralized MRS applications. It also permits fast information propagation, requires fewer cooperative observers of application-specific variables, and reduces the worst-case connectivity requirement, making it more scalable than existing methods. Empirical results demonstrate the scalability and effectiveness of DBP in cooperative target tracking, time synchronization, and localization case studies with hundreds of robots.
The techniques proposed in this dissertation enhance the security and fault-tolerance of MRSs operating in uncertain and adversarial environments, aiding in the development of secure MRSs for emerging applications
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision
Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area
Intrinsic Images and their Applications in Intelligent Systems
The overall goal of the thesis is to research intelligent systems and to provide one more innovative piece in the puzzle towards general artificial intelligence. Because one quickly realizes the importance of computer vision for this endeavor, and in there specifically the need to understand the 3D world through their 2D projections into images, we thoroughly investigate the field of intrinsic images in this thesis and improve the intrinsic decomposition of arbitrary images to enable smarter intelligent systems. We demonstrate the utilization of such a decomposition in the task of relighting, where the intrinsic structure is shown to improve results
Long-duration robot autonomy: From control algorithms to robot design
The transition that robots are experiencing from controlled and often static working environments to unstructured and dynamic settings is unveiling the potential fragility of
the design and control techniques employed to build and program them, respectively. A paramount of example of a discipline that, by construction, deals with robots operating under
unknown and ever-changing conditions is long-duration robot autonomy. In fact, during long-term deployments, robots will find themselves in environmental scenarios which were not planned and accounted for during the design phase. These operating conditions offer a variety of challenges which are not encountered in any other discipline of robotics. This thesis presents control-theoretic techniques and mechanical design principles to be employed while conceiving, building, and programming robotic systems meant to remain operational over sustained amounts of time. Long-duration autonomy is studied and analyzed from two different, yet complementary, perspectives: control algorithms and robot design. In the context of the former, the persistification of robotic tasks is presented. This consists of an optimization-based control framework which allows robots to remain operational over time horizons that are much longer than the ones which would be allowed by the limited
resources of energy with which they can ever be equipped. As regards the mechanical design aspect of long-duration robot autonomy, in the second part of this thesis, the SlothBot, a slow-paced solar-powered wire-traversing robot, is presented. This robot embodies the design principles required by an autonomous robotic system 1in order to remain functional for truly long periods of time, including energy efficiency, design simplicity, and fail-safeness. To conclude, the development of a robotic platform which stands at the intersection of design and control for long-duration autonomy is described. A class of vibration-driven robots, the brushbots, are analyzed both from a mechanical design perspective, and in terms of interaction control capabilities with the environment in which they are deployed.Ph.D
Optimal Routing of Unmanned Vehicles in Persistent Monitoring Missions
Missions such as forest fire monitoring, military surveillance and infrastructure monitoring are referred to as persistent monitoring missions. These missions rely heavily on continual data collection from various locations, referred to as targets. In this dissertation, we consider a framework in which data is collected from the targets with the aid of unmanned aerial vehicles (UAVs). A UAV makes a physical visit to the targets for data collection, and immediately transmits the collected data to a base station for further analysis. Typically, the duration of these monitoring missions is long, and the monitoring vehicles are required to stay in flight for extended periods of time. Therefore, the batteries powering the UAVs must be recharged regularly at a recharging station/depot. From utilitarian and economic points of view, an efficient execution of these missions calls for two requisites: 1) minimizing the time delay between successive data collections at targets; 2) maximizing the total charge/energy drawn from batteries. The maximum time delay between successive data collections at any target is characterized by a function referred to as the walk revisit time, or simply the revisit time. Given a set of targets and a UAV tasked with monitoring the targets, the charge capacity of the battery powering the UAV can be surrogated by the number of visits the UAV can make to the targets without requiring a recharge. To minimize the wastage of energy resources, a charge penalty is imposed on the visits that are unutilized before each recharge. The aim of this work is to find optimal routes for the UAV(s) to visit the targets such that the sum of the revisit time and the charge penalty is minimized. The optimal route planning problem is determined by a number of factors such as the number of UAVs used for monitoring, the aerial platform on which the monitoring UAVs are built, the location of their depots, relative importance of the targets being monitored, etc. In this dissertation, we focus on equally weighted targets and address four different variants of the problem, all of which are computationally extremely challenging.
The variants considered are the following: 1) single UAV with no motion constraints and the depot located at one of the targets; 2) single UAV with curvature constraints on its path and the depot located at one of the targets; 3) single UAV with no motion constraints and its depot stationed at a location different from that of the targets; 4) multiple UAVs with no motion constraints with their depots located at the targets. This dissertation builds on the results of Variant 1; specifically, the characterization of the optimal solutions proved in this dissertation is the main contribution of this dissertation; it lends itself to a new formulation of the same problem that results in significant computational savings. The structural characterization also holds for Variant 2. Inspired by this result, conjectures are provided for the structure of optimal solution for variant 3 and is backed up by extensive numerical simulations. Variant 3 can also be perceived as a special case of targets with different weights/priorities, and therefore, the results developed in this dissertation can potentially be extended to solve a few special cases of the general problem involving arbitrarily weighted targets