Lavindra de Silva

A multi-agent framework for capability-based reconfiguration of industrial assembly systems

Rapidly changing market requirements and shorter product lifecycles demand assembly systems that are able to cope with frequently changing resources, resource capabilities and product specifications. This paper presents a multi-agent framework that can adapt an assembly system in order to cope with such changes. The focus of this work is on the ability to plug resources (such as PLCs) into and out of the system, and dynamically aggregate resource capabilities to form more complex ones as resources are plugged in. In addition, an implementation of the framework on an industrial assembly system is discussed, and some insights are provided into some of the key features that product specification languages ought to have to be useful in real world assembly systems, and into the added value of using the proposed framework.

A Verifiable and Correct-by-Construction Controller for Robots in Human Environments

With the increasing use of domestic and service robots alongside humans, it is now becoming crucial to be able to verify whether robot-software is safe, dependable, and correct. Indeed, in the near future it may well be necessary for robot-software developers to provide safety certifications guaranteeing, e.g. that a hospital nursebot will not move too fast while a person is leaning on it, that the arm of a service robot will not unexpectedly open its gripper while holding a glass, or that there will never be a software deadlock while a robot is navigating in an office. To this end, we have provided a framework and software engineering methodology for developing safe and dependable real-world robotic architectures, with a focus on the functional level--the lowest level of a typical layered robotic architecture--which has all the basic action and perception capabilities such as image processing, obstacle avoidance, and motion control. Unlike past work we address the formal verification of the functional level, which allows providing guarantees that it will not do steps leading to undesirable/disastrous outcomes.

The HATP hierarchical planner: Formalisation and an initial study of its usability and practicality

HTN planners have generally relied on specialised languages for domain and problem representations. To facilitate adoption by other communities such as robotics, however, and integration with real world applications written in standard programming languages, we need HTN planners that are based on more familiar concepts from structured programming, and that come ready with features supporting integration. In this paper, we demonstrate how the HATP (Hierarchical Agent-based Task Planner) HTN planner offers such “syntactic sugar” and some of these features. Moreover, since it has a conceptually distinct syntax compared to traditional HTN planners, we also develop a formalism to unambiguously capture HATPs syntax and an important subset of its semantics, which we then use to compare against the formalism of a well understood family of HTN planners and to show that the former is sound. Finally, we demonstrate that despite quite possibly using “heavier” data structures to naturally capture HATPs syntax/semantics, and thereby facilitate extensions to HATP and integration with other applications, the implementation still performs acceptably.

Interfacing Belief-Desire-Intention Agent Systems with Geometric Reasoning for Robotics and Manufacturing

Unifying the symbolic and geometric representations and algorithms used in AI and robotics is an important challenge for both fields. We take a small step in this direction by presenting an interface between geometric reasoning and a popular class of agent systems, in a way that uses some of the agent’s available constructs and semantics. We then describe how certain kinds of information can be extracted from the geometric model of the world and used in agent reasoning. We motivate our concepts and algorithms within the context of a real-world production system.

Reasoning About the Executability of Goal-Plan Trees

User supplied domain control knowledge in the form of hierarchically structured agent plans is at the heart of a number of approaches to reasoning about action. This knowledge encodes the “standard operating procedures” of an agent for responding to environmental changes, thereby enabling fast and effective action selection. This paper develops mechanisms for reasoning about a set of hierarchical plans and goals, by deriving “summary information” from the conditions on the execution of the basic actions forming the “leaves” of the hierarchy. We provide definitions of necessary and contingent pre-, in-, and postconditions of goals and plans that are consistent with the conditions of the actions forming a plan. Our definitions extend previous work with an account of both deterministic and non-deterministic actions, and with support for specifying that actions and goals within a (single) plan can execute concurrently. Based on our new definitions, we also specify requirements that are useful in scheduling the execution of steps in a set of goal-plan trees. These requirements essentially define conditions that must be protected by any scheduler that interleaves the execution of steps from different goal-plan trees.

An Operational Semantics for a Fragment of PRS

The Procedural Reasoning System (PRS) is arguably the first implementation of the Belief–Desire–Intention (BDI) approach to agent programming. PRS remains extremely influential, directly or indirectly inspiring the development of subsequent BDI agent programming languages. However, perhaps surprisingly given its centrality in the BDI paradigm, PRS lacks a formal operational semantics, making it difficult to determine its expressive power relative to other agent programming languages. This paper takes a first step towards closing this gap, by giving a formal semantics for a significant fragment of PRS. We prove key properties of the semantics relating to PRS-specific programming constructs, and show that even the fragment of PRS we consider is strictly more expressive than the plan constructs found in typical BDI languages.

Process plan controllers for non-deterministic manufacturing systems

Determining the most appropriate means of producing a given product, i.e., which manufacturing and assembly tasks need to be performed in which order and how, is termed process planning. In process planning, abstract manufacturing tasks in a process recipe are matched to available manufacturing resources, e.g., CNC machines and robots, to give an executable process plan. A process plan controller then delegates each operation in the plan to specific manufacturing resources. In this paper we present an approach to the automated computation of process plans and process plan controllers. We extend previous work to support both nondeterministic (i.e., partially controllable) resources, and to allow operations to be performed in parallel on the same part. We show how implicit fairness assumptions can be captured in this setting, and how this impacts the definition of process plans.

A Human-Robot Competition: Towards Evaluating Robots’ Reasoning Abilities for HRI

For effective Human-Robot Interaction (HRI), a robot should be human and human-environment aware. Perspective taking, effort analysis and affordance analysis are some of the core components in such human-centered reasoning. This paper is concerned with the need for benchmarking scenarios to assess the resultant intelligence, when such reasoning blocks function together. Despite the various competitions involving robots, there is a lack of approaches considering the human in their scenarios and in the reasoning processes, especially those targeting HRI. We present a game that is centered upon a human-robot competition, and motivate how our scenario, and the idea of a robot and a human competing, can serve as a benchmark test for both human-aware reasoning as well as inter-robot social intelligence. Based on subjective feedback from participants, we also provide some pointers and ingredients for evaluation matrices.