Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.Unión Europea ARCAS FP7-ICT-287617Unión Europea H2020-ICT-644271Unión europea H2020-ICT-73166

Performance evaluation of a distributed integrative architecture for robotics

The eld of robotics employs a vast amount of coupled sub-systems. These need to interact cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms also require intensive cooperative interactions internally. The architecture proposed lends it- self amenable to problem domains that require rigorous calculations that are usually impeded by the capacity of a single machine, and incompatibility issues between software computing elements. Implementations are abstracted away from the physical hardware for ease of de- velopment and competition in simulation leagues. Monolithic developments are complex, and the desire for decoupled architectures arises. Decoupling also lowers the threshold for using distributed and parallel resources. The ability to re-use and re-combine components on de- mand, therefore is essential, while maintaining the necessary degree of interaction. For this reason we propose to build software components on top of a Service Oriented Architecture (SOA) using Web Services. An additional bene t is platform independence regarding both the operating system and the implementation language. The robot soccer platform as well as the associated simulation leagues are the target domain for the development. Furthermore are machine vision and remote process control related portions of the architecture currently in development and testing for industrial environments. We provide numerical data based on the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted, dynamic languages provides hard information showing the feasibility of Web Services based SOAs even in time critical robotic applications

Exploiting the robot kinematic redundancy for emotion conveyance to humans as a lower priority task

Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task.Postprint (author's final draft

Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization

The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total swarm convergence on some target

Cooperative robots in people guidance mission: DTM model validation and local optimization motion

This work presents a novel approach for optimizing locally the work of cooperative robots and obtaining the minimum displacement of humans in a guiding people mission. This problem is addressed by introducing a “Discrete Time Motion” model (DTM) and a new cost function that minimizes the work required by robots for leading and regrouping people. Furthermore, an analysis of forces actuating among robots and humans is presented throughout simulations of different situations of robot and human configurations and behaviors. Finally, we describe the process of modeling and validation by simulation that have been used to explore the new possibilities of interaction when humans are guided by teams of robots that work cooperatively in urban areas.Peer ReviewedPostprint (published version

Performance optimisation of mobile robots in dynamic environments

This paper presents a robotic simulation system, that combines task allocation and motion planning of multiple mobile robots, for performance optimisation in dynamic environments. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of robot team performance. These two issues become more challenging when there are often operational uncertainties in dynamic environments. We address these issues by proposing an auction-based closed-loop module for task allocation and a bio-inspired intelligent module for motion planning to optimise robot team performance in dynamic environments. The task allocation module is characterised by a closed-loop bid adjustment mechanism to improve the bid accuracy even in light of stochastic disturbances. The motion planning module is bio-inspired intelligent in that it features detection of imminent neighbours and responsiveness of virtual force navigation in dynamic traffic conditions. Simulations show that the proposed system is a practical tool to optimise the operations by a team of robots in dynamic environments. © 2012 IEEE.published_or_final_versionThe IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS 2012), Tianjin, China, 2-4 July 2012. In Proceedings of IEEE VECIMS, 2012, p. 54-5

Cooperative Control of the Dual Gantry-Tau Robot

Utilization of multiple parallel robots operating in the same work place and cooperating on the same job have opened up new challenges in coordination control strategies. Multiple robot control is a natural progression for Parallel Kinematic Machines (PKM) as it offers many of the desirable qualities especially in cooperative arrangements where multiple robots can be associated with an easily reconfigurable parallel machine. These special characteristics allow much faster and precise manipulations especially in manufacturing industries. With the possibility of cooperative control architecture, PKMs will be able to perform many of the tasks currently requiring dual serial robots such as complex assemblies, heavy load sharing and large machining jobs

Development of a miniature robot for swarm robotic application

Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications