PostDoc-Multi

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People

Prof. Javier Alonso-Mora
Prof. Daniela Rus - Massachusetts Institute of Technology (MIT)

Funding

This project is funded at MIT by ONR, SMART and The Boing Company

About the Project

Multi-robot teams can be employed for various tasks, such as surveillance, inspection, and automated factories. In these scenarios, robots may be required to navigate in formation, for example, to maintain a communication network, to collaboratively manipulate an object, or to survey an area. In the near future, assembly systems will incorporate mobile robots to make manufacturing adaptable to changing circumstances, including the manipulation of soft objects that require many hands to control. Within multi-robot navigation, formation control and reconfiguration in presence of moving obstacles remains challenging. This project aims to build a method for collaborative manipulation of objects having variable shape. Various optimization techniques are used to provide good computational efficiency and build algorithms which enable a team of robots to navigate in formation.

The first contribution from this project is a method that can optimise the parameters for a manipulator formation to help avoid moving obstacles and progress towards the goal. This method helps guarantee that the team of mobile robots will not collide with obstacles and a global path planner is provided to build a graph of feasible robot formations. This approach is validated with both a team of aerial vehicles as well as with three mobile manipulators that carry a rigid object.

Another contribution of this project was a local planner that helps in collaborative manipulation of deformable objects.Constraints for both collision avoidance and manipulation are seamlessly integrated in velocity space and a convex optimization is solved. The planner has a constraint to maintain the shape of the object as well. This method can also be used in cases where human operators and manipulators work together to carry an object. In this case either the human trajectory must be known in advance or the human operator acts as the leader while manipulators try to minimise velocity change. The proposed planner has been tested in experiments with a set of deformable objects like rope, foam mat, bed sheet and a bath towel using two to three mobile manipulators.

Project Demonstrations

Funding & Partners

This project is funded at MIT by ONR, SMART and The Boing Company

Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles
J. Alonso-Mora, J. A. DeCastro, V. Raman, D. Rus, H. Kress-Gazit. In Autonomous Robots, Special Issue on Online Decision Making in Multi-Robot Coordination, vol. 42, no. 4, pp. 801–824, 2018.

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In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.

Multi-robot Formation Control and Object Transport in Dynamic Environments via Constrained Optimization
J. Alonso-Mora, S. Baker, D. Rus. In The International Journal of Robotics Research, Vol 36, Issue 9, pp. 1000-1021, 2017.

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We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with static and moving obstacles, and to make progress towards their goal. We describe two variants of the algorithm, one for local motion planning and one for global path planning. The local planner first computes a large obstacle-free convex region in a neighborhood of the robots, embedded in position-time space. Then, the parameters of the formation are optimized therein by solving a constrained optimization, via sequential convex programming. The robots navigate towards the optimized formation with individual controllers that account for their dynamics. The idea is extended to global path planning by sampling convex regions in free position space and connecting them if a transition in formation is possible - computed via the constrained optimization. The path of lowest cost to the goal is then found via graph search. The method applies to ground and aerial vehicles navigating in two- and three-dimensional environments among static and dynamic obstacles, allows for reconfiguration, and is efficient and scalable with the number of robots. In particular, we consider two applications, a team of aerial vehicles navigating in formation, and a small team of mobile manipulators that collaboratively carry an object. The approach is verified in experiments with a team of three mobile manipulators and in simulations with a team of up to sixteen Micro Air Vehicles (quadrotors).

Distributed Multi-Robot Navigation in Formation among Obstacles: A Geometric and Optimization Approach with Consensus
J. Alonso-Mora, E. Montijano, M. Schwager, D. Rus. In , in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (ICRA), 2016.

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This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors.

Multi-robot navigation in formation via sequential convex programming
J. Alonso-Mora, S. Baker, D. Rus. In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2015.

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This paper presents a method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The method is local and computes the optimal parameters for the formation within a neighborhood of the robots, allowing for reconfigurations, when required, by considering a set of target formations. The method consists of first computing the largest collision-free convex polytope in a neighborhood of the robots, followed by a constrained optimization via sequential convex programming where the optimal parameters for the formation are obtained. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performed well in simulations with a large team of quadrators and in experiments with two mobile manipulators carrying a rigid object.

Collision-Free Reactive Mission and Motion Planning for Multi-Robot Systems
J. DeCastro, J. Alonso-Mora, V. Raman, D. Rus, H. Kress-Gezit. In Proc. of the Int. Symposium on Robotics Research (ISRR), 2015.

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This paper describes a holistic method for automatically synthesizing controllers for a team of robots operating in an environment shared with other agents. The proposed approach builds on recent advances in Reactive Mission Planning using Linear Temporal Logic, and Local Motion Planning using convex optimization. A local planner enforces the dynamic constraints of the robot and guarantees collision avoidance in 2D and 3D workspaces. A reactive mission planner takes a high-level specification that captures complex motion sequencing, and generates a correct-by-construction controller guaranteed to achieve the specified behavior and be reactive to sensor events. If there is no controller that fulfills the specification because of possible deadlock in the local planner, a minimal set of human-readable assumptions is generated as a certificate of the conditions on deadlock where the task is guaranteed. This is truly a synergistic method: the low-level motion planner enables scalability of the high-level plan synthesis with respect to dynamic obstacles, and the high-level mission planner enforces correctness of the low-level motion. We provide formal guarantees for our approach and demonstrate it via physical experiments with ground robots and simulations with a team of quadrotors.

Local motion planning for collaborative manipulation of deformable objects in dynamic environments
J. Alonso-Mora, R. Knepper, R. Siegwart, D. Rus. In Proc. of the IEEE Int. Conf. Robotics and Automation (ICRA), 2015.

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This paper presents a formalism that exploits deformability during manipulation of soft objects by robot teams. A hybrid centralized/distributed approach restricts centralized planning to high-level global guidance of the object for consensus. Low-level control is thus delegated to the individual manipulator robots, which retain manipulation and collision avoidance guarantees by passing forces to one another through the object. A distributed receding horizon planner provides local control, formulated as a convex optimization problem in velocity space and incorporating constraints for both collision avoidance and shape maintenance. We demonstrate teams of mobile manipulators autonomously carrying various deformable objects.

Optimal Control and Optimization Methods for Multi-robot Systems
J. Alonso-Mora, K. Savla, D. Rus. In Tutorial on Multi-Robot Systems at Robotics Science and Systems (RSS), 2015.

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