Multi-Robot Workcell Layout and Task Sequencing for Collision and Wear
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Solution Overview
Problem
Existing methods for configuring multiple robots in a shared workspace are inefficient, requiring significant manual effort and computation time, often leading to suboptimal work throughput and collision risks, and are not easily adaptable to environmental changes.
Innovation Solution
A global optimizer using a multi-variable, mixed integer optimization algorithm co-optimizes robot configurations across non-homogenous parameters such as base position, task allocation, and target sequences, minimizing wear, collision risk, and energy consumption, while allowing for dynamic task allocation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If task-level de-confliction is used to ensure collision-free operation, then robot safety is improved, but work throughput deteriorates due to idle time
Solution Approach 1:
The system dynamically adjusts robot motion plans in real-time based on actual task progress and environmental changes. Instead of static task-level allocation, the motion planning continuously adapts trajectories and timing to maximize parallel operation while maintaining collision-free constraints, allowing robots to operate more efficiently without compromising safety
Solution Approach 2:
The system implements continuous feedback loops where robot positions, task completion status, and environmental conditions are constantly monitored. This feedback enables real-time adjustments to motion plans, allowing the system to respond to actual operational conditions rather than relying on pre-programmed conservative timing, thereby improving throughput while maintaining safety
2Device complexity
If engineers manually divide and optimize sub-problems independently, then implementation complexity is reduced, but overall optimization deteriorates due to lack of co-optimization
Solution Approach 1:
The system merges task allocation, motion planning, and collision avoidance into a unified optimization framework. Rather than treating these as separate sub-problems to be solved independently, the system co-optimizes all aspects simultaneously, considering the interdependencies between task assignment, trajectories, and timing to achieve globally optimal performance
Solution Approach 2:
The motion planning system serves multiple functions simultaneously: it performs task allocation, generates collision-free trajectories, optimizes timing, and adapts to environmental changes. This multi-functional approach eliminates the need for separate manual optimization of each sub-problem while achieving superior overall performance
3Reliability
If iterative simulation is used to validate robot workflows, then collision safety is improved, but computation time deteriorates significantly
Solution Approach 1:
The system performs comprehensive collision validation and optimization in advance during the motion planning phase. By pre-calculating collision-free trajectories and validating workflows before execution, the system eliminates the need for time-consuming iterative simulations during runtime, achieving both safety and efficiency
Solution Approach 2:
The system replaces physical iterative simulation with computational optimization algorithms that directly calculate optimal collision-free trajectories. Instead of repeatedly simulating and adjusting motion plans, the system uses advanced algorithms to compute the optimal solution in a single pass, dramatically reducing computation time while maintaining safety
4Area of stationary object
If robots operate in shared workspace with overlapping range of motion, then workspace utilization is improved, but collision risk deteriorates
Solution Approach 1:
The system dynamically coordinates robot motions in real-time, continuously adjusting trajectories and timing based on actual positions and task progress. This dynamic coordination allows robots to operate in overlapping workspaces by adaptively resolving potential conflicts, maximizing workspace utilization while maintaining collision-free operation
Solution Approach 2:
The system ensures continuous collision-free operation through real-time monitoring and adaptive motion planning. By maintaining constant awareness of robot positions and continuously adjusting trajectories as needed, the system enables sustained parallel operation in shared workspaces without interruption or collision
Data Source
AI summary
Solutions for multi-robot configurations are co-optimized for wear, collision and optionally for performance and/or energy expenditure, across a set of non-homogenous parameters based on a set of tasks to be performed by a set of robots. Non-homogenous parameters may include two or more of: the respective base position and orientation of the robots, an allocation of tasks to respective robots, respective target sequences and/or trajectories for the robots. Such may be executed pre-runtime. Output may include for each robot: workcell layout, an ordered list or vector of targets, optionally dwell time durations at respective targets, and paths or trajectories between each pair of consecutive targets. Output may provide a complete, executable, solution to the problem, which in the absence of variability in timing, can be used to control the robots without any modification. A genetic algorithm, e.g., Differential Evolution, may optionally be used in generating a population of candidate solutions.