Automated composition of motion primitives for multi-robot systems from safe LTL specifications

Saha, Indranil; Ramaithitima, Rattanachai; Kumar, Vijay; Pappas, George J.; Seshia, Sanjit A.

doi:10.1109/iros.2014.6942758

Cited by 106 publications

(103 citation statements)

References 31 publications

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“…To synthesize optimal trajectory for robot R i , we invoke the algorithm described in Section III-D in [20] with input motion planning problem…”

Section: A Finding Optimal Trajectories and Obstacle Robotsmentioning

confidence: 99%

“…The algorithm first synthesizes the cost-optimal trajectories for all the robots independently by using the SMT-based algorithm introduced in [20]. Then the algorithm uses the SMT-based priority assignment algorithm to assign priorities to the robots in such a way that the ordering induced by the priority assignment enables the robots not to block the trajectories of the other robots.…”

Section: Introductionmentioning

confidence: 99%

“…The outcome of the priority assignment algorithm is a set of groups of robots, where the robots with the same priority are included in the same group. If a group has more than one robot, we synthesize the costoptimal trajectory of the group of robots together using the SMT-based multi-robot motion planning algorithm described in [20]. Now, one can dispatch the robots to their destinations according to their priority (robots with the same priority are dispatched together), and it is guaranteed that all robots will be able to reach their destination successfully.…”

Section: Introductionmentioning

confidence: 99%

“…Implan provides an automated mechanism to synthesize motion plans in the form of software code that can be directly used to control the robots using a centralized computer. We compare our incremental trajectory planning algorithm with the SMT-based multi-robot motion planning algorithm in [20]. For a group of 6 robots, the monolithic approach in [20] takes more than 3 hours to generate the (sub-optimal) trajectories, whereas our incremental algorithm can generate the collision free optimal trajectories in less than 40 mins.…”

Section: Introductionmentioning

confidence: 99%

“…We compare our incremental trajectory planning algorithm with the SMT-based multi-robot motion planning algorithm in [20]. For a group of 6 robots, the monolithic approach in [20] takes more than 3 hours to generate the (sub-optimal) trajectories, whereas our incremental algorithm can generate the collision free optimal trajectories in less than 40 mins. Our algorithm also scales well with number of robots.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

Saha

Ramaithitima

Kumar

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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Abstract-We consider the collision-free motion planning problem for a group of robots using a library of motion primitives. To cope with the complexity of the problem, we introduce an incremental algorithm based on an SMT solver, where we divide the robots into small groups based on a priority assignment algorithm. The priority assignment algorithm assigns priorities to the robots in such a way that the robots do not block the cost-optimal trajectories of the other robots. While the priority assignment algorithm attempts to assign distinct priorities to the robots, the algorithm ends up with assigning the same priority to some robots due to the dependencies among themselves. The algorithm includes the robots with the same priority in the same group. Our incremental algorithm then considers the robot groups one by one based on their priority and synthesizes the trajectories for the group of robots together. While synthesizing the trajectories for the robots in one group, the algorithm considers the higher priority robots as dynamic obstacles, and introduces a minimal delay in executing the cost-optimal trajectories to avoid collision with the higher priority robots. We apply our method to synthesize trajectories for a group of quadrotors in our lab space. Experimental results show that we can synthesize trajectories for tens of robots with complex dynamics in a reasonable time.

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“…To synthesize optimal trajectory for robot R i , we invoke the algorithm described in Section III-D in [20] with input motion planning problem…”

Section: A Finding Optimal Trajectories and Obstacle Robotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

Saha

Ramaithitima

Kumar

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

Self Cite

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System Design in the Cyber‐Physical Era

Nuzzo

Sangiovanni‐Vincentelli

2017

Nanoelectronics

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Temporal Logic Verification of Stochastic Systems Using Barrier Certificates

Jagtap

Soudjani

2018

Lecture Notes in Computer Science

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We study formal synthesis of control policies for discrete-time stochastic control systems against complex temporal properties. Our goal is to synthesize a control policy for the system together with a lower bound on the probability that the system satisfies a complex temporal property. The desired properties of the system are expressed as a fragment of linear temporal logic (LTL), called safe-LTL over finite traces. We propose leveraging control barrier certificates which alleviate the issue of the curse of dimensionality associated with discretization-based approaches existing in the literature. We show how control barrier certificates can be used for synthesizing policies while guaranteeing lower bounds on the probability of satisfaction for the given property. Our approach decomposes negation of the specification into sequential reachabilities and then finds control barrier certificates for computing upper-bounds on the reachability probabilities. Control policies associated with these barrier certificates are then combined as a hybrid control policy for the concrete system that guarantees a lower bound on the probability of satisfaction of the property. We distinguish uncountable and finite input sets in the computation of barrier certificates. For the former, control barrier certificates can be computed using sum-of-square optimization. For the latter, we develop a computational method based on counter-example guided inductive synthesis. We demonstrate the efectiveness of the proposed approach on a room temperature control and lane keeping of a vehicle modeled as a four-dimensional single-track kinematic model. We compare our results with the discretization-based methods in the literatures.

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Automated composition of motion primitives for multi-robot systems from safe LTL specifications

Cited by 106 publications

References 31 publications

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

System Design in the Cyber‐Physical Era

Temporal Logic Verification of Stochastic Systems Using Barrier Certificates

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