Goal Position Research Articles

Modeling the Dynamics of Collective Behavior in a Reflexive Game with an Arbitrary Number of Leaders

An oligopoly with an arbitrary number of Stackelberg leaders under incomplete, asymmetrical agents' awareness and inadequacy of their predictions of competitors' actions is considered. Models of individual decision-making processes by agents are studied. The reflexive games theory and collective behavior theory are the theoretical basis for construction and analytical study process models. They complement each other in that reflexive games allow using the collective behavior procedures and the results of agents' reflections, leading to a Nash equilibrium. The dynamic decision-making process considered repeated static games on a range of agents' feasible responses to the expected actions of the environment, considering current economic restrictions and competitiveness in each game. Each reflexive agent in each game calculates its current goal position and changes its state, taking steps towards the current position of the goal to obtain positive profit or minimize losses. Sufficient conditions for the convergence of processes in discrete time for the case of linear costs of agents and linear demand is the main result of this work. New analytical expressions for the agents' current steps' ranges guarantee the convergence of the collective behavior models to static Nash equilibrium is obtained. That allows each agent to maximize their profit, assuming common knowledge among the agents. The processes when the agent chooses their best response are also analyzed. The latter may not give converging trajectories. The case of the duopoly in comparison with modern results is discussed in detail. Necessary mathematical lemmas, statements, and their proofs are presented.

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

SwarmItFIX (self-reconfigurable intelligent swarm fixtures) is a multi-agent setup mainly used as a robotic fixture for large Sheet metal machining operations. A Constraint Satisfaction Problem (CSP) based planning model is utilized currently for computing the locomotion sequence of multiple agents of the SwarmItFIX. But the SwarmItFIX faces several challenges with the current planner as it fails on several occasions. Moreover, the current planner computes only the goal positions of the base agent, not the path. To overcome these issues, a novel hierarchical planner is proposed, which employs Monte Carlo and SARSA TD based model-free Reinforcement Learning (RL) algorithms for the computation of locomotion sequences of head and base agents, respectively. These methods hold two distinct features when compared with the existing methods (i) the transition model is not required for obtaining the locomotion sequence of the computational agent, and (ii) the state-space of the computational agent become scalable. The obtained results show that the proposed planner is capable of delivering optimal makespan for effective fixturing during the sheet metal milling process.

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach. Our main contribution is a new SAT-based MAPF solver called MDD-SAT, that is directly aimed to optimally solve the MAPF problem under the sum-of-costs objective function. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, MDD-SAT is able to generate a reasonable number of Boolean variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. In addition, we show that concepts applicable in search-based solvers like ICTS and ICBS are applicable in the SAT-based approach as well. Specifically, we integrate independence detection, a generic technique for decomposing an MAPF instance into independent subproblems, into our SAT-based approach, and we design a relaxation of our optimal SAT-based solver that results in a bounded suboptimal SAT-based solver. Experimental evaluation on several domains shows that there are many scenarios where our SAT-based methods outperform state-of-the-art sum-of-costs search-based solvers, such as variants of the ICTS and ICBS algorithms.

Energy Efficient Local Path Planning Algorithm Based on Predictive Artificial Potential Field

Energy efficiency is one of the most important parameters in transportation electrification. It allows to improve the production rate due to longer operation without charging or decrease the cost related to transportation. To provide collision-free operation in unknown or various environment, the local path planning algorithm should be considered. An Artificial Potential Field (APF) algorithm is commonly used for this task, however it provides unsmooth and oscillating motion of autonomous ground vehicle (AGV), and is prone to being trapped in a local minimum, e.g, dead-end. In such a case, the energy used to achieve the goal position is higher than necessary. In this paper, the energy-efficient local path planning algorithm is proposed. Future movement prediction has been introduced to APF to allow AGV to bypass obstacles in advance. A novel method for local minimum avoidance is introduced. It is based on placement of virtual obstacles called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top quarks</i> in critical areas. These obstacles provide additional repulsive force for the APF based path planner. Considering the predicted stagnation-free path of the AGV, the new temporary goal for APF is selected. Such a combination allows to reduce traveled route length, improve its smoothness, and bypass local minima. The proposed Predictive Artificial Potential Field (PAPF) algorithm has been examined using Husarion ROSbot 2.0 PRO mobile robot, and the obtained results in form of videos are also attached as supplementary files. In comparison to original APF, the proposed path planning algorithm allows to reduce the used electric power by 21.4 %. PAPF provided a shorter path by up to 8.73 % and shorter time to reach the goal position by up to 40.23 %. The movement of the AGV is also much smoother in a case of usage of the proposed algorithm, and the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top quarks</i> -based local minimum avoidance mechanism allows to bypass the local minima.

POMDP-Based Real-Time Path Planning for Manipulation of Multiple Microparticles via Optoelectronic Tweezers.

With high throughput and high flexibility, optoelectronic tweezers (OETs) hold huge potential for massively parallel micromanipulation. However, the trajectory of the virtual electrode has been planned in advance in most synchronous manipulations for multiple targets based on an optically induced dielectrophoresis (ODEP) mechanism, which is insufficient to ensure the stability and efficiency in an environment with potential collision risk. In this paper, a synchronously discretized manipulation method based on a centralized and decoupled path planner is proposed for transporting microparticles of different types with an OET platform. An approach based on the Kuhn-Munkres algorithm is utilized to achieve the goal assignment between target microparticles and goal positions. With the assistance of a visual feedback module, a path planning approach based on the POMDP algorithm dynamically determines the motion strategies of the particle movement to avoid potential collisions. The geometrical parameters of the virtual electrodes are optimized for different types of particles with the goal of maximum transport speed. The experiments of micropatterning with different morphologies and transporting multiple microparticles (e.g., polystyrene microspheres and 3T3 cells) to goal positions are performed. These results demonstrate that the proposed manipulation method based on optoelectronic tweezers is effective for multicell transport and promises to be used in biomedical manipulation tasks with high flexibility and efficiency.

Perception-Aware Receding Horizon Trajectory Planning for Multicopters With Visual-Inertial Odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach is able to fly the vehicle to a goal position at high speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory&#x2019;s aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera&#x2019;s view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.

Control of mobile robot formations using A-star algorithm and artificial potential fields

Formations or groups of robots become essential in cases where a single robot is insufficient to satisfy a given task. With an increasingly automated world, studies on various topics related to robotics have been carried out in both the industrial and academic arenas. In this paper, the control of the formation of differential mobile robots based on the leader-follower approach is presented. The leader's movement is based on the least cost path obtained by the A-star algorithm, thus ensuring a safe and shortest possible route for the leader. Follower robots track the leader's position in real time. Based on this information and the desired distance and angle values, the leader robot is followed. To ensure that the followers do not collide with each other and with the obstacles in the environment, a controller based on Artificial Potential Fields is designed. Stability analysis using Lyapunov theory is performed on the linearized model of the system. To verify the implemented technique, a simulator was designed using the MATLAB programming language. Seven experiments are conducted under different conditions to show the performance of the approach. The distance and orientation errors are less than 0.1 meters and 0.1 radians, respectively. Overall, mobile robots are able to reach the goal position, maintaining the desired formation, in finite time.

Planning Catching Movements: Advantages of Expertise, Visibility and Self-Throwing

In a ball catching task, the catcher guides their hand to the ball’s future trajectory. The hand may start to move even before the exact position is known, and the interceptive movement may be corrected online. Using a recent method for detecting the phases of catching movements we investigate how juggling experience, self-throwing, and delayed visibility of the ball, influence the timing of the hand’s trajectory. Specifically, we analyze the time from which the goal position of the movement is known, i.e., the time from which the movement becomes smooth. Seventeen jugglers and twenty controls caught ten balls per each of eight conditions. The results indicate that experts’ catching movements acquire the smooth nature of goal-directed movements earlier than novices catching movements do.

Intelligent Path Planning Approach for Autonomous Mobile Robot

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.

End in view: Joint end-state comfort depends on gaze and extraversion.

This study investigated how humans adapt to a partner's movement in a joint pick-and-place task and examined the role of gaze behavior and personality traits in adapting to a partner. Two participants sitting side-by-side transported a cup from one end of a table to the other. The participant sitting on the left (the agent) moved the cup to an intermediate position from where the participant sitting on the right (the partner) transported it to a goal position with varying orientations. Hand, finger, cup movements and gaze behavior were recorded synchronously via motion tracking and portable eye tracking devices. Results showed interindividual differences in the extent of the agents' motor adaptation to the joint action goal, which were accompanied by differences in gaze patterns. The longer agents directed their gaze to a cue indicating the goal orientation, the more they adapted the rotation of the cup's handle when placing it at the intermediate position. Personality trait assessment showed that higher extraverted tendencies to strive for social potency went along with more adaptation to the joint goal. These results indicate that agents who consider their partner's end-state comfort use their gaze to gather more information about the joint action goal compared to agents who do not. Moreover, the disposition to enjoy leadership and make decisions in interpersonal situations seems to play a role in determining who adapts to a partner's task in joint action.

Study of Goal Oriented Obstacle Avoidance for Mobile Robot

—This paper suggests simultaneous mechanism which makes UGV to secure its safety and recreate optimum path right after obstacle avoidance occurs. Research target and sensors is shown and the suggested optimum region path algorithm is presented. Also, this paper provides the performance of suggested algorithm by simulation, and will propose the direction of the future plans. The fixed and varying weight algorithm satisfy the constraints, once the robot arrives at the goal position in the simulation. However, the fixed weight algorithm was not able to drive on the shortest path, which is optimal, with many other successful weight values. On the other hand, the varying weight algorithm successfully generated the optimized path by changing the weight values in term of local environment. Especially, in the second simulation, the robot was able to safely arrive to the goal with little time period. This paper proposed VTV and Local Optimal path algorithm that the robot can generate the optimal path to the goal position without colliding obstacles as adapting Fuzzy controller which continuously optimizes the weight values of the cost function in terms of local environment

Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping

Robotic manipulation of a bulky object is challenging due to the limited kinematics and payload of the manipulator. In this study, a robot realizes the manipulation of general-shaped bulky objects utilizing the contact with the environment. We propose a hierarchical manipulation planner that effectively combined three manipulation styles, namely, pivoting, tumbling, and regrasping. In our proposed method, we first generate a set of superimposed planar segments on the object surface to obtain an object pose in stable contact with the table, and a set of points on the object surface for the end-effectors (EEFs) of a dual-arm manipulator to stably grasp the object. Object manipulation can be realized by solving a graph, considering the kinematic constraints of pivoting and tumbling. For pivoting, we consider two supporting styles: stable support (SP) and unstable support (USP). Our proposed method manipulates large and heavy objects by selectively using the two different support styles of pivoting and tumbling according to the conditions on the table area. In addition, it can effectively avoid the limitation arising due to the arm kinematics by regrasping the object. We experimentally demonstrate that a dual-arm manipulator can move an object from the initial to goal position within a limited area on the table, avoiding obstacles placed on the table.

Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods

This study integrates path planning, fuzzy theory, neural networks, image processing, range sensors, webcam, global navigation satellite system (GNSS), and real-time kinematic (RTK) positioning system into an intelligent wheeled mobile robot (WMR) for outdoor patrolling. The robot system uses ultrasound sensors, laser sensors, and fuzzy controllers to detect obstacles and avoid them. The starting position and the goal position of the WMR in an outdoor environment are given by the GNSS RTK positioning system. Real-time position correction of the robot is performed through the differential global positioning system. The robot system applies the ant algorithm and the Dijkstra algorithm to find the shortest path for patrol tasks. The convolutional neural network image processing is utilized to identify intruders that are appearing in the patrol path. When the WMR detects an intruder by the face detection and recognition methods, the robot captures the photo of this person by the webcam and acquires the location information of this person by the RTK positioning system. Then the WMR sends the location and photo of the intruder to the control center by Wi-Fi and asks for help.

Conflict-Based Search For Optimal Multi-Agent Path Finding

In the multi agent path finding problem (MAPF) paths should be found for several agents, each with a different start and goal position such that agents do not collide. Previous optimal solvers applied global A*-based searches. We present a new search algorithm called Conflict Based Search (CBS). CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. In many cases this reformulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks. Experimental results on various problems shows a speedup of up to a full order of magnitude over previous approaches.

Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.

From Multi-Agent Pathfinding to 3D Pipe Routing

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that some recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on real-world PR instances. This opens up a new direction of industrial relevance for the MAPF research community.

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

This work investigates on the coordinated locomotion between a ceiling-mounted serial manipulator and two SwarmItFIX robots. The former holds the machining tool as an end effector, and the other two robots act as swarm robotic fixtures in a sheet metal milling process. A novel offline coordination planner which follows the hierarchical based hybrid type decentralized planning strategy has been proposed. Motion of the serial manipulator and SwarmItFIX robots’ coordinated locomotion are divided into three sub-problems, viz, trajectory planning of serial manipulator, task planning of SwarmItFIX robots, and homogenous prioritized multi-robot path planning of SwarmItFIX robots. Mathematical formulation of all the three sub-problems is developed and presented in this paper. A hexagonal segment that fits inside the boundaries of the workspace is considered as the machining trajectory. The tool velocity is assumed to be constant as it improves the quality of machining. The results obtained from the proposed planner is found to be efficient as the task planning module has computed the precise support locations and support duration for the SwarmItFIX robots. The multi-robot path planning module of the planner computes the optimal collision-free paths of SwarmItFIX robots for all goal positions. Finally, trajectories of SwarmItFIX robots are found to be completely in-line with the trajectory of tool center point (TCP) of the serial manipulator.

Finding Optimal Solutions to Sokoban Using Instance Dependent Pattern Databases

Pattern databases have been successfully applied to several problems. Their use assumes that the goal state is known, and once the pattern database is built, commonly it can be used by all instances. However, in Sokoban, before solving the puzzle, the goal position of each stone is unknown. Moreover, each Sokoban instance has its own state space search. In this paper we apply pattern databases to Sokoban. The proposed approach uses an instance decomposition, that allows multiple possible goal states to be abstracted into a single state. Thus, an instance dependent pattern database is employed. Experiments with the standard set of instances show that the proposed approach overcomes the current best lower bounds in initial states for several instances. Furthermore, three of these new best lower bounds match exactly with the optimal solution length. Finally, we run experiments of 5 million explored states for each instance. Nine instances were solved with optimality guarantees, while only four instances were solved under the same conditions by previous methods.

Meta-Agent Conflict-Based Search For Optimal Multi-Agent Path Finding

The task in the multi-agent path finding problem (MAPF) isto find paths for multiple agents, each with a different startand goal position, such that agents do not collide. It is possibleto solve this problem optimally with algorithms that arebased on the A* algorithm. Recently, we proposed an alternativealgorithm called Conflict-Based Search (CBS) (Sharonet al. 2012), which was shown to outperform the A*-basedalgorithms in some cases. CBS is a two-level algorithm. Atthe high level, a search is performed on a tree based on conflictsbetween agents. At the low level, a search is performedonly for a single agent at a time. While in some cases CBSis very efficient, in other cases it is worse than A*-based algorithms.This paper focuses on the latter case by generalizingCBS to Meta-Agent CBS (MA-CBS). The main idea isto couple groups of agents into meta-agents if the number ofinternal conflicts between them exceeds a given bound. MACBSacts as a framework that can run on top of any completeMAPF solver. We analyze our new approach and provideexperimental results demonstrating that it outperforms basicCBS and other A*-based optimal solvers in many cases.

Conceptual Comparison of Compilation-Based Solvers for Multi-Agent Path Finding: MIP vs. SAT

The task in multi-agent path finding (MAPF) is to find paths through which agents can navigate from their starting positions to given individual goal positions. The combination of two additional requirements makes the problem challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. We summarize and compare main ideas of contemporary compilation-based solvers for MAPF using MIP and SAT formalisms.