Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M

Development of a Socially Believable Multi-Robot Solution from Town to Home

Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services—shopping delivery and garbage collection—showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Airborne mapping of complex obstacles using 2D Splinegon

This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter and to track the obstacle so as to extract the map of the obstacle. This Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. This description takes into account the fact that the vehicle is made to fly around the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon technique. This splinegon technique, the most efficient and a robust way to estimate the boundary of a curved nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Abstractions, Analysis Techniques, and Synthesis of Scalable Control Strategies for Robot Swarms

Tasks that require parallelism, redundancy, and adaptation to dynamic, possibly hazardous environments can potentially be performed very efficiently and robustly by a swarm robotic system. Such a system would consist of hundreds or thousands of anonymous, resource-constrained robots that operate autonomously, with little to no direct human supervision. The massive parallelism of a swarm would allow it to perform effectively in the event of robot failures, and the simplicity of individual robots facilitates a low unit cost. Key challenges in the development of swarm robotic systems include the accurate prediction of swarm behavior and the design of robot controllers that can be proven to produce a desired macroscopic outcome. The controllers should be scalable, meaning that they ensure system operation regardless of the swarm size. This thesis presents a comprehensive approach to modeling a swarm robotic system, analyzing its performance, and synthesizing scalable control policies that cause the populations of different swarm elements to evolve in a specified way that obeys time and efficiency constraints. The control policies are decentralized, computed a priori, implementable on robots with limited sensing and communication capabilities, and have theoretical guarantees on performance. To facilitate this framework of abstraction and top-down controller synthesis, the swarm is designed to emulate a system of chemically reacting molecules. The majority of this work considers well-mixed systems when there are interaction-dependent task transitions, with some modeling and analysis extensions to spatially inhomogeneous systems. The methodology is applied to the design of a swarm task allocation approach that does not rely on inter-robot communication, a reconfigurable manufacturing system, and a cooperative transport strategy for groups of robots. The third application incorporates observations from a novel experimental study of the mechanics of cooperative retrieval in Aphaenogaster cockerelli ants. The correctness of the abstractions and the correspondence of the evolution of the controlled system to the target behavior are validated with computer simulations. The investigated applications form the building blocks for a versatile swarm system with integrated capabilities that have performance guarantees