5,359 research outputs found
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
A New Approach to Time-Optimal Path Parameterization based on Reachability Analysis
Time-Optimal Path Parameterization (TOPP) is a well-studied problem in
robotics and has a wide range of applications. There are two main families of
methods to address TOPP: Numerical Integration (NI) and Convex Optimization
(CO). NI-based methods are fast but difficult to implement and suffer from
robustness issues, while CO-based approaches are more robust but at the same
time significantly slower. Here we propose a new approach to TOPP based on
Reachability Analysis (RA). The key insight is to recursively compute reachable
and controllable sets at discretized positions on the path by solving small
Linear Programs (LPs). The resulting algorithm is faster than NI-based methods
and as robust as CO-based ones (100% success rate), as confirmed by extensive
numerical evaluations. Moreover, the proposed approach offers unique additional
benefits: Admissible Velocity Propagation and robustness to parametric
uncertainty can be derived from it in a simple and natural way.Comment: 15 pages, 9 figure
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