Real-time motion planning is important for robot movement. In motion planning, path search and collision detection are two performance bottlenecks. In this paper, we adopt a range-based matching scheme with ternary content-addressable memories (TCAMs) to accelerate the processes of both nearest neighbor search and collision detection. In our approach, the nearest node search and collision detection can be both processed in a few TCAM lookup cycles. The evaluation shows that the TCAM-based accelerator is 236× faster than CPU for motion planning tasks. It is 5.4× faster and at least 8.8× more energy-efficient than a state-of-the-art dedicated ASIC-based accelerator.

Research on accelerators for robotics is increasing. This article introduces the kinematics, motion planning and collision detection algorithms and our accelerators in robotics, and then analyzes their advantages, disadvantages and bottlenecks. In view of the shortcomings of the existing accelerators, this paper will show a series accelerators named "DaDu" that we have proposed. For kinematics, we have proposed Dadu [1] to accelerate the inverse kinematics algorithm, which achieves 1700x speedup than the CPU implementation, 30x speedup than the GPU implementation, and 776x higher energy efficiency than the GPU implementation. For motion planning, we have proposed Dadu-P [2] to accelerate the PRM algorithm. It can get 26.5x speedup than an existing CPU-based approach for collision detection. Furthermore, with an incremental approach, the performance of motion planning can further be improved by 10x while the solution quality is degraded by 10% only. For the collision detection algorithm in motion planning, the proposed accelerator Dadu-CD [3] elaborates the in-memory processing architecture, achieving at least 5x speedup than Dadu-P in the total planning time and 9.55x lower energy consumption than Dadu-P.

As a critical operation in robotics, motion planning consumes lots of time and energy, especially in a dynamic environment. Through approaches based on general-purpose processors, it is hard to get a valid planning in real time. We present an accelerator to speed up collision detection, which costs over 90% of the computation time in motion planning. Via the octree-based roadmap representation, the accelerator can be reconfigured online and support large roadmaps. We in addition propose an effective algorithm to update the roadmap in a dynamic environment, together with a batched incremental processing approach to reduce the complexity of collision detection. Experimental results show that our accelerator achieves 26.5X speedup than an existing CPU-based approach. With the incremental approach, the performance further improves by 10X while the solution quality is degraded by 10% only.

Multi-granularity memory system provides multiple access granularities for the applications with various spatial localities. In the multi-granularity access pattern, the one-size-bandwidth NoC design cannot utilize the bandwidth efficiently. We propose a novel NoC design, called BoDNoC, which can merge multiple narrow subnets to provide various bandwidths for access data. The new design also adopts an optimization algorithm to take full advantage of bandwidth provision. Experimental results show that BoDNoC can improve the throughput by 23.5% and reduce the energy consumption by 37.2% in comparison with one-size-bandwidth NoC design.

Kinematics is the basis of robotic control, which manages the robots' movement, walking and balancing. As a critical part of Kinematics, the Inverse Kinematics (IK) will consume more time and energy to figure out the solution with the degrees of freedom increase. It goes beyond the ability of general-purpose processor based methods to provide real-time IK solver for manipulators with high degree of freedom. In this paper, we present a novel parallel algorithm, Quick-IK, based on the Jacobian transpose method. Via speculative searching in parallel, Quick-IK can reduce the number of iterations by 97% for the baseline Jacobian transpose method. In addition, we propose a novel specialized architecture, IKAcc, to boost the energy efficiency of Quick-IK through hardware acceleration. The evaluation shows that IKAcc can solve IK problem in 12 milliseconds for a 100 degrees of freedom manipulator. In addition, IKAcc can achieve 1700x performance speed-up over the CPU implementation of the original Jacobian transpose method and 30x speedup over the GPU implementation of Quick-IK. At same time, IKAcc achieves about 776x higher energy efficiency than the GPU implementation of Quick-IK.