Multi-Robot Workcell Layout and Motion Planning for Collision-Free Throughput
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Solution Overview
Problem
Current methods for optimizing multi-robot configurations in shared workspaces are inefficient, often requiring manual intervention and extensive computational time to ensure collision-free motion planning, leading to suboptimal work throughput and requiring expert intervention for modifications in the workspace.
Innovation Solution
A global optimizer system that co-optimizes across non-homogeneous parameters such as robot placement, task allocation, and motion planning using a multi-variable, mixed integer optimization algorithm like Differential Evolution, to provide optimized robot configurations and motion plans that minimize collisions and maximize efficiency in multi-robot operational environments.
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 robots being idle for significant portions of time
Solution Approach 1:
The system performs preliminary motion planning for multiple robots simultaneously, computing collision-free trajectories for all robots before execution. This pre-computation allows robots to operate independently without idle waiting, as the complete coordinated plan is established in advance, resolving the contradiction between safety and throughput.
Solution Approach 2:
The system dynamically adjusts robot trajectories and timing within the pre-computed plan to maximize parallel operation. By allowing flexible timing adjustments while maintaining collision-free constraints, the system enables robots to work simultaneously in interference regions rather than sequentially, improving throughput while preserving safety.
2Ease of manufacture
If manual programming is used to define interference regions and coordinate robots, then implementation simplicity is improved, but programming time and complexity deteriorate
Solution Approach 1:
The system automatically computes motion plans for multiple robots without requiring manual programming of interference regions or coordination logic. The algorithm self-determines safe trajectories and timing, eliminating the need for engineers to manually define interference regions and coordinate robot behavior, thus reducing both programming time and complexity.
Solution Approach 2:
The system replaces manual engineering processes with automated computational algorithms. Instead of engineers manually programming collision avoidance logic, the system uses motion planning algorithms to compute coordinated trajectories, substituting mechanical/manual intervention with automated intelligent computation.
3Measurement precision
If iterative simulation is used to validate robot workflows, then collision detection accuracy is improved, but computation time deteriorates to many hours
Solution Approach 1:
The system performs preliminary motion planning that computes complete collision-free trajectories for all robots before execution. By planning all robot motions simultaneously and verifying collision-free operation in advance, the system achieves accurate collision detection without requiring multiple iterative simulations during runtime, reducing computation time from hours to minutes.
Solution Approach 2:
The system merges the motion planning of multiple robots into a single coordinated computation. Instead of separately simulating and validating each robot's trajectory through multiple iterative simulations, the system computes all robot trajectories together in one optimization process, achieving the same collision detection accuracy with significantly reduced computation time.
4Adaptability or versatility
If workspace modifications are made, then adaptability to new requirements is improved, but re-validation time deteriorates as the entire workflow must be re-validated
Solution Approach 1:
The system performs preliminary motion planning that computes complete coordinated trajectories for all robots based on the current workspace configuration. When workspace modifications occur, the system can re-compute the motion plan from scratch using the updated configuration, rather than re-validating the entire workflow through iterative simulation. This approach maintains adaptability while significantly reducing re-validation time.
Data Source
AI summary
Solutions for multi-robot configurations are co-optimized, to at least some degree, across a set of non-homogenous parameters based on a given set of tasks to be performed by robots in a multi-robot operational environment. 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.