Hybrid conditional planning for robotic applications

2020 ◽  
pp. 027836492096378
Author(s):  
Ahmed Nouman ◽  
Volkan Patoglu ◽  
Esra Erdem
Keyword(s):  
Cognitive Abilities ◽  
Initial State ◽  
Dynamic Simulations ◽  
Partial Observability ◽  
Goal State ◽  
Incomplete Knowledge ◽  
Sensing Actions ◽  
Action Language ◽  
Sat Solver ◽  
Conditional Planning

Robots who have partial observability of and incomplete knowledge about their environments may have to consider contingencies while planning, and thus necessitate cognitive abilities beyond classical planning. Moreover, during planning, they need to consider continuous feasibility checks for executability of the plans in the real world. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability, by considering all contingencies and by utilizing sensing actions to gather relevant knowledge when needed. A conditional plan is essentially a tree of actions where each branch of the tree represents a possible execution of actuation actions and sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning by integrating feasibility checks into executability conditions of actions. We introduce a parallel offline algorithm, called HCPlan, for computing hybrid conditional plans. HCPlan relies on modeling deterministic effects of actuation actions and non-deterministic effects of sensing actions in the causality-based action language [Formula: see text]. Branches of a hybrid conditional plan are computed in parallel using a SAT solver, where continuous feasibility checks are performed as needed. We develop a comprehensive benchmark suite and introduce new evaluation metrics for hybrid conditional planning. We evaluate HCPlan with extensive experiments in terms of computational efficiency and plan quality. We perform experiments to compare HCPlan with other related conditional planners and approaches to deal with contingencies due to incomplete knowledge. We further demonstrate the applicability and usefulness of HCPlan in service robotics applications, through dynamic simulations and physical implementations.

scholarly journals Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming

2007 ◽  
Vol 7 (4) ◽  
pp. 377-450 ◽  
Author(s):  
PHAN HUY TU ◽  
TRAN CAO SON ◽  
CHITTA BARAL
Keyword(s):  
Incomplete Information ◽  
Answer Set Programming ◽  
Planning Problem ◽  
Possible World ◽  
Initial State ◽  
Possible World Semantics ◽  
Sensing Actions ◽  
Action Theories ◽  
Causal Laws ◽  
Answer Set

AbstractWe extend the 0-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation isNP-complete. We then present an answer set programming based conditional planner, called ASCP, that is capable of generating both conformant plans and conditional plans in the presence of sensing actions, incomplete information about the initial state, and static causal laws. We prove the correctness of our implementation and argue that our planner is sound and complete with respect to the proposed approximation. Finally, we present experimental results comparing ASCP to other planners.

scholarly journals Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional Agent

2008 ◽  
Vol 18 (3) ◽  
pp. 409-421 ◽  
Author(s):  
Silvana Petruseva
Keyword(s):  
Shortest Path ◽  
Emotional State ◽  
Linear Time ◽  
Decisive Role ◽  
Time Algorithm ◽  
Shortest Path Problem ◽  
Adaptive Array ◽  
Agent Architecture ◽  
Initial State ◽  
Goal State

Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional AgentThe paper presents an algorithm which solves the shortest path problem in an arbitrary deterministic environment withnstates with an emotional agent in linear time. The algorithm originates from an algorithm which in exponential time solves the same problem, and the agent architecture used for solving the problem is an NN-CAA architecture (neural network crossbar adaptive array). By implementing emotion learning, the linear time algorithm is obtained and the agent architecture is modified. The complexity of the algorithm without operations for initiation in general does not depend on the number of statesn, but only on the length of the shortest path. Depending on the position of the goal state, the complexity can be at mostO (n).It can be concluded that the choice of the function which evaluates the emotional state of the agent plays a decisive role in solving the problem efficiently. That function should give as detailed information as possible about the consequences of the agent's actions, starting even from the initial state. In this way the function implements properties of human emotions.

Generation of Probabilistically Optimal Paths for Nonholonomic Needle Insertion

2016 ◽  
Author(s):  
Jianping Lin ◽  
Wooram Park
Keyword(s):  
Cost Function ◽  
Optimal Path ◽  
Target Position ◽  
Nonholonomic Constraints ◽  
Needle Insertion ◽  
Initial State ◽  
Goal State ◽  
Medical Needles ◽  
The Cost ◽  
Planning Problems

Rapidly-exploring Random Tree (RRT) is a sampling-based algorithm which is designed for path planning problems. It is efficient to handle high-dimensional configuration space (C-space) and nonholonomic constraints. Under the nonholonomic constraints, the RRT can generate paths between an initial state and a goal state while avoiding obstacles. Since this framework assumes that a system is deterministic, more improvement should be added when the method is applied to a system with uncertainty. In robotic systems with motion uncertainty, probability for successful targeting and obstacle avoidance are more suitable measurement than the deterministic distance between the robot system and the target position. In this paper, the probabilistic targeting error is defined as a root-mean-square (RMS) distance between the system to the desired target. The proximity of the obstacle to the system is also defined as an averaged distance of obstacles to the robotic system. Then, we consider a cost function that is a sum of the targeting error and the obstacle proximity. By numerically minimizing the cost, we can obtain the optimal path. In this paper, a method for efficient evaluation and minimization of this cost function is proposed and the proposed method is applied to nonholonomic flexible medical needles for performance tests.

scholarly journals Penerapan Metode Best First Search Pada Permainan Tic Tac Toe

2017 ◽  
pp. 1-6
Author(s):  
Harvei Desmon Hutahaean
Keyword(s):  
Problem Solving ◽  
State Space ◽  
Directed Graph ◽  
The State ◽  
Initial State ◽  
Problem Space ◽  
Goal State ◽  
Best First Search ◽  
Problem Solving Behavior

Search is the process of finding solutions in a problem until a solution or goal is found, or a movement in the state-space to search for trajectories from initial-state to goal-state. In a TIC TAC Toe game the process of finding a space situation is not enough to automate problem-solving behavior, in each of these situations there are only a limited number of choices that a player can make. The problems that will be faced can be solved by searching from the choices available, supported by the usual way of resolving. Best First Search works by searching for a directed graph which each node represents a point in a problem space.

scholarly journals Learning First-Order Representations for Planning from Black Box States: New Results

2021 ◽  
Author(s):  
Ivan D. Rodriguez ◽  
Blai Bonet ◽  
Javier Romero ◽  
Hector Geffner
Keyword(s):  
State Space ◽  
Partial Information ◽  
Large Family ◽  
The State ◽  
Initial State ◽  
New Approach ◽  
First Order ◽  
Sat Solver ◽  
State Graphs ◽  
Domain Description

Recently Bonet and Geffner have shown that first-order representations for planning domains can be learned from the structure of the state space without any prior knowledge about the action schemas or domain predicates. For this, the learning problem is formulated as the search for a simplest first-order domain description D that along with information about instances I_i (number of objects and initial state) determine state space graphs G(P_i) that match the observed state graphs G_i where P_i = (D, I_i). The search is cast and solved approximately by means of a SAT solver that is called over a large family of propositional theories that differ just in the parameters encoding the possible number of action schemas and domain predicates, their arities, and the number of objects. In this work, we push the limits of these learners by moving to an answer set programming (ASP) encoding using the CLINGO system. The new encodings are more transparent and concise, extending the range of possible models while facilitating their exploration. We show that the domains introduced by Bonet and Geffner can be solved more efficiently in the new approach, often optimally, and furthermore, that the approach can be easily extended to handle partial information about the state graphs as well as noise that prevents some states from being distinguished.

scholarly journals Hybrid conditional planning using answer set programming

2017 ◽  
Vol 17 (5-6) ◽  
pp. 1027-1047 ◽  
Author(s):  
IBRAHIM FARUK YALCINER ◽  
AHMED NOUMAN ◽  
VOLKAN PATOGLU ◽  
ESRA ERDEM
Keyword(s):  
Answer Set Programming ◽  
Partial Knowledge ◽  
Service Robotics ◽  
Sensing Actions ◽  
Performance Comparisons ◽  
Answer Set ◽  
Conditional Planning ◽  
Formal Representations

AbstractWe introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains.

"Coo" Vocalizations in Stumptailed Macaques: a Controlled Functional Analysis

Behaviour ◽  
1991 ◽  
Vol 119 (1-2) ◽  
pp. 143-160 ◽  
Author(s):  
Frans B.M. De Waal ◽  
K.A. Bauers
Keyword(s):  
Communication Systems ◽  
Internal State ◽  
Indirect Evidence ◽  
Social Contact ◽  
Control Measures ◽  
Initial State ◽  
Goal State ◽  
Focal Animal ◽  
The Social ◽  
Contact Calls

AbstractCoo vocalizations have been described in the communication systems of several macaque species. Their use has been suggested to reflect the emitter's "internal state", with regard to motivation for social contact. The idea that the production of Coos reflects a desire for contact has never been critically evaluated; the effectiveness of these signals in facilitating contact between the emitter and others has not been demonstrated. We designed a study to test the function of Coo vocalizations in adult stumptailed macaques (Macaca arctoides), using control measures similar to those first employed in research on reconciliation behavior in macaques (DE WAAL & YOSHIHARA, 1983). We found that the probability of an approach leading to friendly contact was significantly greater following a female's Coo vocalizations than it was during control trials. Analysis of the social context of the focal animal as it emits a Coo (its "initial state"), provides indirect evidence that Coos are "contact calls". We found that stumptails were significantly more likely to be alone than in contact with others when they emitted a Coo. Females were more likely to repeat their Coo vocalizations if they did not achieve friendly contact within 30 s. This result is in line with the view of Coo vocalizations as part of a feed-back loop with affiliative social contact as the goal state. Coos were produced by other adult females at a higher rate and with a shorter latency following the focal female's Coos than they were in control trials. Response vocalizations may affirm, to the "cooing" female, the presence and location of other troop members. On the basis of the non-vocal behavioral changes that these signals predict, Coos may be viewed as social tools, used to influence conspecifics, in addition to whatever their use may reflect regarding an emitter's "internal state". We suggest that the use of Coos to solicit approach may be a convention for the avoidance of aggression.

Motion Analysis of Robot Arm With Movement Restriction

2016 ◽  
Author(s):  
Celeste Colberg Poley ◽  
Balakumar Balachandran
Keyword(s):  
Path Planning ◽  
Degrees Of Freedom ◽  
Robotic Manipulator ◽  
Robot Arm ◽  
Initial State ◽  
Movement Restriction ◽  
End Effector ◽  
Goal State ◽  
Planning Algorithm ◽  
Manipulator Arm

Medical robots are increasingly being used to assist surgeons during procedures requiring precision. As reported in the literature, surgeons have been opting for minimally invasive surgery, as it reduces patient complications, overall patient recovery time, and hospital time for the patient. Robotic manipulators can be used to overcome natural limitations related to vision and human dexterity, and allow surgeons to transcend these limitations without having to sacrifice improvement in patient outcome. A desirable attribute of surgical robots is maneuverability similar to the human arm. The KUKA DLR Lightweight Robot Arm (LWR), with seven degrees of freedom, retains many of these human-like dexterity traits. Due to the KUKA robot arms maneuverability and flexibility, it is well-suited for intricate tasks based upon motion analyses and modeling of the compliance to path trajectory in addition to the overall smoothness of the path. This robot may be further programmed to be effective and precise for surgical applications. In the studies reported here, a unique Rapidly exploring Randomized Tree (RRT) based path-planning algorithm is developed and this algorithm is used to generate path plans between an initial state and a goal state for simulated models of robotic manipulator arms. Along with constraints, the RRT algorithm has been implemented to find paths for the chosen kinematic or dynamic robotic manipulator arm. Similar techniques are to be used to analyze the KUKA LWR IV+ system. Motion analyses have been carried out with consideration of motion trajectories and all possible locations of the end effector with unique constraints applied to the system. In these simulations, the Denavit-Hartenberg parameters were recorded, with special attention to movement restrictions. The results of the RRT paths generation, analysis of the manipulator arm trajectories, and simulations allow one to better determine the location of the end-effector at any given point in time and location. From this foundation, the generation of path-planning restrictions for the KUKA robots path programming is expected to take into account surgically restricted dangerous or undesirable zones. In future work, the trajectories of the KUKA robot and other manipulator arms are to be compared with the data available in the literature. This work holds promising implications for the improved use of such robot systems in surgical applications. For example, precise pre-programmed robotic movements are expected to be particularly helpful for surgeries in tight, anatomically restricted sites, with adjacent delicate tissues. Ultimately, it is expected that this type of novel robotic application will greatly aid surgeons in improving the precision and safety of surgical procedures, by reducing potential complications and minimizing potential nicks and tears, and working towards giving the surgeons the same ease that they have with traditional surgery.

A rewriting framework and logic for activities subject to regulations

2015 ◽  
Vol 27 (3) ◽  
pp. 332-375 ◽  
Author(s):  
MAX KANOVICH ◽  
TAJANA BAN KIRIGIN ◽  
VIVEK NIGAM ◽  
ANDRE SCEDROV ◽  
CAROLYN TALCOTT ◽  
...  
Keyword(s):  
Human Error ◽  
Linear Logic ◽  
Formal Semantics ◽  
Time Constraints ◽  
Negative Consequences ◽  
Initial State ◽  
Goal State ◽  
Model Based ◽  
Compliance Problem ◽  
Planning Problems

Activities such as clinical investigations (CIs) or financial processes are subject to regulations to ensure quality of results and avoid negative consequences. Regulations may be imposed by multiple governmental agencies as well as by institutional policies and protocols. Due to the complexity of both regulations and activities, there is great potential for violation due to human error, misunderstanding, or even intent. Executable formal models of regulations, protocols and activities can form the foundation for automated assistants to aid planning, monitoring and compliance checking. We propose a model based on multiset rewriting where time is discrete and is specified by timestamps attached to facts. Actions, as well as initial, goal and critical states may be constrained by means of relative time constraints. Moreover, actions may have non-deterministic effects, i.e. they may have different outcomes whenever applied. We present a formal semantics of our model based on focused proofs of linear logic with definitions. We also determine the computational complexity of various planning problems. Plan compliance problem, for example, is the problem of finding a plan that leads from an initial state to a desired goal state without reaching any undesired critical state. We consider all actions to be balanced, i.e. their pre- and post-conditions have the same number of facts. Under this assumption on actions, we show that the plan compliance problem is PSPACE-complete when all actions have only deterministic effects and is EXPTIME-complete when actions may have non-deterministic effects. Finally, we show that the restrictions on the form of actions and time constraints taken in the specification of our model are necessary for decidability of the planning problems.

scholarly journals Planning and Acting with Non-Deterministic Events: Navigating between Safe States

2020 ◽  
Vol 34 (06) ◽  
pp. 9802-9809
Author(s):  
Lukas Chrpa ◽  
Jakub Gemrot ◽  
Martin Pilat
Keyword(s):  
Real World ◽  
Automated Planning ◽  
Dynamic Environments ◽  
Initial State ◽  
Goal State

Automated Planning addresses the problem of finding a sequence of actions, a plan, transforming the environment from its initial state to some goal state. In real-world environments, exogenous events might occur and might modify the environment without agent's consent. Besides disrupting agent's plan, events might hinder agent's pursuit towards its goals and even cause damage (e.g. destroying the robot).In this paper, we leverage the notion of Safe States in dynamic environments under presence of non-deterministic exogenous events that might eventually cause dead-ends (e.g. “damage” the agent) if the agent is not careful while executing its plan. We introduce a technique for generating plans that constrains the number of consecutive “unsafe” actions in a plan and a technique for generating “robust” plans that effectively evade event effects. Combination of both approaches plans and executes robust plans between safe states. We empirically show that such an approach effectively navigates the agent towards its goals in spite of presence of dead-ends.

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