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This paper presents a decentralized algorithm that generates continuous-time trajectory online for a swarm of robots based upon model predictive control. To generate collision-free trajectory, temporally distinct safe regions are formed such that the robots are confined to move within these safe regions to avoid collisions with one another. The distinct safe regions are temporally linked by generating a B-spline. Additionally, to ensure that collisions are avoided, collision-regions that the robots have to stay outside are also generated distinctly. A non linear program (NLP) with an objective to make the robots stay outside the collision-regions and stay within the safe regions is formulated. The algorithm was tested in simulations on Gazebo with aerial robots. The simulated results suggest that the proposed algorithm is computationally efficient and can be used for online planning in moderate sized multi-robot systems.

Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.